Research Article Electroencephalography Signal Grouping...
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Hindawi Publishing Corporation Journal of Applied Mathematics Volume 2013, Article ID 754539, 9 pages http://dx.doi.org/10.1155/2013/754539 Research Article Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI Tae-Ju Lee, Seung-Min Park, and Kwee-Bo Sim School of Electrical and Electronics Engineering, College of Engineering, Chung-Ang University, Heukseok-dong, Dongjak-gu, Seoul 156-756, Republic of Korea Correspondence should be addressed to Kwee-Bo Sim; [email protected] Received 28 June 2013; Revised 27 September 2013; Accepted 27 September 2013 Academic Editor: Zong Woo Geem Copyright © 2013 Tae-Ju Lee et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. is paper presents a heuristic method for electroencephalography (EEG) grouping and feature classification using harmony search (HS) for improving the accuracy of the brain-computer interface (BCI) system. EEG, a noninvasive BCI method, uses many electrodes on the scalp, and a large number of electrodes make the resulting analysis difficult. In addition, traditional EEG analysis cannot handle multiple stimuli. On the other hand, the classification method using the EEG signal has a low accuracy. To solve these problems, we use a heuristic approach to reduce the complexities in multichannel problems and classification. In this study, we build a group of stimuli using the HS algorithm. en, the features from common spatial patterns are classified by the HS classifier. To confirm the proposed method, we perform experiments using 64-channel EEG equipment. e subjects are subjected to three kinds of stimuli: audio, visual, and motion. Each stimulus is applied alone or in combination with the others. e acquired signals are processed by the proposed method. e classification results in an accuracy of approximately 63%. We conclude that the heuristic approach using the HS algorithm on the BCI is beneficial for EEG signal analysis. 1. Introduction e brain-computer interface (BCI) is a system that connects a human brain and a machine. Without using an external control device, humans would be able to control devices such as brain-controlled mobile robots, wheelchairs, and robot manipulators [1–3] with their thoughts. erefore, we greatly anticipate that the BCI system will become a technology not only for the average person but also for the disabled including people suffering from a neurodegenerative disorder such as amyotrophic lateral sclerosis and brainstem stroke [4]. To implement the BCI, various signal acquisition methods have been proposed. Among the various BCI systems, the electroencephalography (EEG)-type BCI is promising tech- nology. e BCI system based on EEG does not require sur- gical operation and is considered as the ultimate goal of the systems for practical use because of its relative convenience [5]. Furthermore, the EEG signal has a higher temporal reso- lution than functional magnetic resonance imaging (fMRI) or functional near-infrared spectroscopy (fNIRS) signals. is characteristic provides the detailed states of brain activity in a very short time. However, several restrictions exist in the use of the EEG signals in the BCI system. Although the EEG signal has a high temporal resolution, it has very poor spatial resolution. If we want to observe the details of the brain state in a small area, we have to use many electrodes on the scalp, which will result in channel increment; that is, channel increment is an ineluctable requirement and induces a “curse of dimensionality.” Similar to other systems, the BCI system consists of three stages: sensing, signal processing, and applications. e brain-wave signal from a sensor is used for the application according to the class of the signal. e curse of dimension causes a problem in signal processing, which adds more com- plexity, increases the computational time, and complicates the classification. To avoid a high-dimensional computational problem resulting from the use of many channels, existing research suggests two primary methods: the multimodal integration method [6, 7] and the channel-selection method. e first method, which combines several signals, requires the solution to several limitations. fMRI or fNIRS requires
Transcript of Research Article Electroencephalography Signal Grouping...
Hindawi Publishing CorporationJournal of Applied MathematicsVolume 2013 Article ID 754539 9 pageshttpdxdoiorg1011552013754539
Research ArticleElectroencephalography Signal Grouping and FeatureClassification Using Harmony Search for BCI
Tae-Ju Lee Seung-Min Park and Kwee-Bo Sim
School of Electrical and Electronics Engineering College of Engineering Chung-Ang University Heukseok-dong Dongjak-guSeoul 156-756 Republic of Korea
Correspondence should be addressed to Kwee-Bo Sim kbsimcauackr
Received 28 June 2013 Revised 27 September 2013 Accepted 27 September 2013
Academic Editor Zong Woo Geem
Copyright copy 2013 Tae-Ju Lee et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited
This paper presents a heuristicmethod for electroencephalography (EEG) grouping and feature classification using harmony search(HS) for improving the accuracy of the brain-computer interface (BCI) system EEG a noninvasive BCI method uses manyelectrodes on the scalp and a large number of electrodes make the resulting analysis difficult In addition traditional EEG analysiscannot handle multiple stimuli On the other hand the classification method using the EEG signal has a low accuracy To solvethese problems we use a heuristic approach to reduce the complexities in multichannel problems and classification In this studywe build a group of stimuli using the HS algorithm Then the features from common spatial patterns are classified by the HSclassifier To confirm the proposed method we perform experiments using 64-channel EEG equipmentThe subjects are subjectedto three kinds of stimuli audio visual and motion Each stimulus is applied alone or in combination with the others The acquiredsignals are processed by the proposed methodThe classification results in an accuracy of approximately 63We conclude that theheuristic approach using the HS algorithm on the BCI is beneficial for EEG signal analysis
1 Introduction
The brain-computer interface (BCI) is a system that connectsa human brain and a machine Without using an externalcontrol device humans would be able to control devices suchas brain-controlled mobile robots wheelchairs and robotmanipulators [1ndash3] with their thoughts Therefore we greatlyanticipate that the BCI system will become a technology notonly for the average person but also for the disabled includingpeople suffering from a neurodegenerative disorder suchas amyotrophic lateral sclerosis and brainstem stroke [4]To implement the BCI various signal acquisition methodshave been proposed Among the various BCI systems theelectroencephalography (EEG)-type BCI is promising tech-nology The BCI system based on EEG does not require sur-gical operation and is considered as the ultimate goal of thesystems for practical use because of its relative convenience[5] Furthermore the EEG signal has a higher temporal reso-lution than functionalmagnetic resonance imaging (fMRI) orfunctional near-infrared spectroscopy (fNIRS) signals Thischaracteristic provides the detailed states of brain activity
in a very short time However several restrictions exist inthe use of the EEG signals in the BCI system Although theEEG signal has a high temporal resolution it has very poorspatial resolution If we want to observe the details of thebrain state in a small area we have to use many electrodeson the scalp which will result in channel increment that ischannel increment is an ineluctable requirement and inducesa ldquocurse of dimensionalityrdquo
Similar to other systems the BCI system consists ofthree stages sensing signal processing and applicationsThebrain-wave signal from a sensor is used for the applicationaccording to the class of the signal The curse of dimensioncauses a problem in signal processing which addsmore com-plexity increases the computational time and complicatesthe classification To avoid a high-dimensional computationalproblem resulting from the use of many channels existingresearch suggests two primary methods the multimodalintegration method [6 7] and the channel-selection methodThe first method which combines several signals requiresthe solution to several limitations fMRI or fNIRS requires
2 Journal of Applied Mathematics
a larger space for the equipment and building the entiresystem is expensive [8] Further the multimodal experi-mental method can be potentially contaminated [9] On theother hand the channel-selection method is a preferableand convenient method although some data are droppedThis method restricts the channels using the brain area ormathematical models [10ndash12] However this method focusesonly on one stimulus at a time Therefore it cannot handlesimultaneous stimuli in the human brain The BCI systemthat employs the EEG signal for actual use overcomes thedisadvantage of the curse of dimension
In the BCI system the performance is dependent onfeature extraction and classification From the raw-datasignal we can extract the EEG features In this process thecommon spatial pattern (CSP) algorithm is commonly usedas a feature extraction algorithm [13 14] In the classificationeven though many approaches have been attempted and thesupport vector machine (SVM) is preferred [15ndash17] a dom-inant algorithm is absent The linear discriminant analysisand the original SVM algorithm are linear classificationsand their performance in nonlinear EEG signal analysis ispoor The SVM algorithm with a kernel function is not onlyredundant when the dimension becomes higher but alsodifficult in unsupervised cases The artificial neural networkis powerful and can classify nonlinear data however it suffersfrom a weakness because the length of time needed forlearning cannot be approximated Thus a new approach toBCI feature classification is needed
The present paper presents a solution to the two problemsof conventional channel selection and feature classificationusing the harmony search (HS) algorithm a music-inspiredmetaheuristic algorithm The metaheuristic algorithm findsthe solution using rules and randomness thus it does notrequire too many computational resources [18] In non-deterministic polynomial-time hard (NP-hard) problems wecan solve the problem using the heuristic algorithm fasterthan that using the exact computational method The HSalgorithm one of the nature-inspired heuristic algorithmspossesses advantages that are not available in the otherheuristic algorithms HS can maintain diversity thus thesearch solution effectively and easily escapes from the localoptimum [19] In addition the HS algorithm is easy to usehas a simple structure and requires only two parameters Byusing the HS algorithm we can determine the group of EEGsignal channels to eliminate the restriction of the curse ofdimension in signal processing and develop a classificationfor BCI system applications In Section 2 we present thetheoretical background of the HS and feature extractionalgorithms In Section 3 we show the proposed methodsSections 4 and 5 present the experiment and analysis of thechannel grouping and classification We conclude this paperin Section 6
2 Theoretical Background
21 Harmony Search Algorithm As the name suggests theHS algorithm originates from musical harmony [20] If weassume that the variables are the instruments the pitches of
the instruments are the values of the variables Similar tothe orchestra player who determines the harmony to createa good balance the HS algorithm searches the best stateof a given problem defined by an objective function TheHS algorithm has a harmony memory (HM) that saves theprevious results and its structure is described in a matrixform
[[[[[[
11990911
11990912
1199091119873
11990921
11990922
1199092119873
119909HMS1
119909HMS2
119909HMS119873
]]]]]]
(1)
where 119909119894119899is the selected solution of the 119899th variable HMS
is the HM size and 119873 is the number of variables The HMobtains the latest best result thus the HM members changewhen the result of the new value is better than the worstresult in the HM The new value is selected in three waysselected byHM randomly selected from the range of possiblesolutions or selected by a local area search called the pitchadjustment The selection by the HM is performed usingthe HM consideration rate (HMCR) The pitch adjustmenta mutation of the HM member also has an occurrencerate which is called the pitch adjustment rate (PAR) Therandom search and the pitch adjustment prevent prematureconvergence andmaintain the diversity of the solutionTheseprocesses are represented as follows
119909New119899
larr997888
119909119899isin 119883119899 119883 is possible solution wp 119877Random
119909119899isin 1199091119899 1199092119899 119909HMS
119899 wp 119877Memory
119909119894119899plusmn 119898 wp 119877pitch
(2)where 119898 is the pitch adjustment size and 119877Random 119877Memoryand 119877pitch are the probability of the random search HMCRand PAR respectively
22 Parameter-Setting-Free Harmony Search In the basicHS algorithm principle we use several parameters namelyHMS HMCR and PAR Generally the HMS value rangesfrom 10 to 100 Although this value depends on the iterationnumber the result is not sensitive to the HMS In contrastthe HMCR and PAR control the result of the HS algorithmTo select a proper parameter value some investigations havebeen conducted [21 22] The parameter-setting-free (PSF)HS proposed by Geem and Sim [23] is one of the parametercontrol methods for automatic selection of the parametersThis method uses onemorememory to represent themethodthat generates theHMvaluesThememory is the same as thatof the HMThen we obtain the new parameter values on thebasis of the following equations
HMCR119899= Number of 119909
119899made by ldquorandom selectionrdquo
HMS
PAR119899= Number of 119909
119899made by ldquopitch adjustmentrdquo
HMS(3)
Journal of Applied Mathematics 3
In each iteration time the PSF HS calculates the HMCRand PAR Then we obtain the proper adaptive parametersUsing this PSF technique the value to be initialized is onlythe HMS
23 Common Spatial Patterns The CSP algorithm is one ofthe useful methods for feature extraction of the EEG signalsFor two different classes 119888
1and 1198882 we obtain multiple spatial
filters that maximize the variance of one class and minimizethat of the other class The spatial filter matrix119882 is obtainedby solving the following equation [24]
wlowast = arg maxwisin119877119873
wT119877s|1198881wwT119877s|1198882w
(4)
where 119877s|1198881 and 119877s|1198882 are the covariance matrices of signal sgiven in each class The 119882 matrix consists of 119871 eigenvectorsof wlowast Thus the final goal of the CSP is to obtain the lineartransform s which is represented by the following equation
s = 119882Ts (5)
3 Methodology
31 Mean Square Error and Euclidean Matrices To derivethe objective function the mean square error (MSE) andthe Euclidean distances were used The MSE matrix wasused for grouping the EEG channels A low MSE means thatthe two signals are almost the same Moreover if the MSEis zero the two signals are the same Therefore the groupwith a minimum MSE has similar characteristics and can beregarded to have the same reaction for the same stimulus Forthe grouping of the signals we first normalized the EEG dataacquired from 64 channels We divided the EEG signals withthe maximum values in each channel Then the elementsin the 119895th row and 119896th column of the MSE matrix wererepresented as follows (here the diagonal members of theMSE matrix were zero)
MSE119895119896
= 1Tsum119905
119904119895(119905) minus 119904
119896(119905)2 where 119895 119896 = 1 2 64
(6)
In (6) 119904119895(119905) and 119904
119896(119905) are the normalized signal vectors of
the119895th and 119896th channels at time 119905 respectivelyFor the feature extraction we used the Euclidean distance
matrix Similar to grouping we assumed that the sameclass members were located close to one another Fromthis assumption we determined the group with the shortestoverall distance After feature extraction was completed thefeatures were expressed in a vector space Then the (119895 119896)elements of the Euclidean distance matrix were representedas follows (the diagonal of the MSE matrix is zero and 119904lowastrepresents the mean feature vector in the 119871-119863 space)
Dist119895119896
= 1003816100381610038161003816100381610038161003816100381610038161003816119904lowast
119895minus 119904lowast119896
10038161003816100381610038161003816100381610038161003816100381610038162 where 119895 119896 = 1 2 119871 (7)
32 Minimum-Seeking Harmony Search To represent themember of a group or class we used a binary switch vector
(BSV) which indicates which channel or vector was selectedIf the 119895thmember of the BSV is one the 119895th channel or vectoris included in the group or class Therefore we determine theBSV values that generate the minimum result of the objectivefunction using the HS algorithmThe objective function usedin this study is the following
arg min119863119904lowastℎ
1 + 119863119904lowastℎ
(8)
119863 = 12sum119895
sum119896
119889119895119896 (9)
In (8) 119904lowast is the channel number or feature selected by theBSV and ℎ is a constant determined by 119904lowast The numeral ldquo1rdquoin the numerator is used to prevent the value of (8) frombecoming zero 119863 is the summation of the entire MSE orthe distances selected by the BSV In (9) 119889
119895119896is the selected
member of the MSE matrix or the Euclidean distance matrixselected by the BSV
BSV contains the information in which the data espe-cially the channels or features are selected To apply the BSVto the HS algorithm we can use the binary HS algorithm[25] However the binary HS algorithm has difficulty inapplying pitch adjustment and requires more memory thanthe basic HS algorithm This characteristic affects the speedperformance and accuracy of the binary HS algorithmThusin the HS we converted each 119903 binary number in the BSV toa 2119903-radixThen the solution ranged from 0 to 2119903 minus1 and thepitch adjustment size 119898 was 119903 In this study we used 119903 = 4When the value was above the range of the pitch adjustmentwe used the circular method
The value of ℎ was determined by the following processwhen the value of 119904lowast is from 0 to 119878lowast the objective function isrepresented as follows
Objective function =
10ℎ = infin if 119904lowast = 01 + 01ℎ = 1 if 119904lowast = 1
1 + 119863119904lowastℎ
if 1 lt 119904lowast lt 119878lowast(10)
We assumed that the optimal value of 119904lowast was 119904 and thevalue of the objective function with 119904 was represented as 119891
119904
Then if 119904lowast = 119904 minus 1 the objective function satisfies thefollowing equation
1 + 119863119904minus1
(119904 minus 1)ℎ=1 + 119863
119904minus sum119895119889119895119896
119904ℎ ( 119904119904 minus 1)
ℎ
= (119891119904minussum119895119889119895119896
119904ℎ )(1 + 1119904 minus 1)
ℎ
(11)
From the definition of 119904 the following relationship shouldbe satisfied
1003816100381610038161003816119891119904minus1 minus 119891119904
1003816100381610038161003816 gt 0 (12)
4 Journal of Applied Mathematics
If 119904lowast = 119904 + 1 the objective function satisfies the followingequation
1 + 119863119904+1
(119904 + 1)ℎ=1 + 119863
119904+ sum119895119889119895119896
119904ℎ ( 119904119904 + 1)
ℎ
= (119891119904+sum119895119889119895119896
119904ℎ )(1 minus 1119904 + 1)
ℎ
(13)
From the definition of 119904 the following relationship shouldalso be satisfied
1003816100381610038161003816119891119904+1 minus 119891119904
1003816100381610038161003816 gt 0 (14)
From (11) and (12) we obtain the following relationship
119891119904gt (1 + (1119904 minus 1))ℎ (sum
119895119889119895119896119904)
(1 + (1119904 minus 1))ℎ minus 1 (15)
From (10) 119891119904le 1 Then (15) is transformed into the
following relationship
119904ℎ minus (119904 minus 1)ℎ gt sum119895
119889119895119896 (16)
The same process can be applied to (13) and (14) From(13) (14) and (16) we obtain the following
119878lowast
sum119895=1
119889119895119896+ (119904 minus 1)ℎ lt sℎ lt
119878lowast+1
sum119895=1
119889119895119896+ (119904 + 1)ℎ (17)
Equation (17) should be satisfied for all 119896 values There-foresum119878lowast
119895=1119889119895119896andsum119878lowast+1
119895=1119889119895119896can be respectively converted to
the maximum summation of 119889119895119896 which has a value of one
in the BSV (119889maxone) and the minimum summation of 119889119895119896
which has a value of zero in the BSV (119889minzero) Equation (17)is rewritten as follows
119889maxone + (119904 minus 1)ℎ lt sℎ lt 119889minzero + (119904 + 1)ℎ (18)
In this equation 119889maxone and 119889minzero are constant and 119904is determined by the HS process Therefore the value of ℎshould satisfy (18)
4 Experiment
In the experiments we used a commercial product theNeuroscan EEG signal acquisition device (Neuroscan systemCompumedics USA) to acquire the EEG signalsThis deviceconsists of the Synamps2 and Stim2 hardware and thesoftware for acquisition and preprocessing is Curry 7 Weused an EEG cap with 64 electrodes for the EEG signals andfive additional electrodes for noise reduction The subjectswere three healthy men in their mid-twenties The subjectswere selected without using any selection criteria to ensuregenerality of the results All subjects had undergone oneor more brain-wave experiments not related to this studyThe subjects were fully aware of the entire experimental
FP1 FP2FPz
Fz F2 F4 F6 F8F1F3F5F7
FCz FC2 FC4 FC6 FT8FC1FC3FC5FT7
Cz C2 C4 C6 T8C1C3C5T7
CPz CP2 CP4 CP6 TP8CP1CP3CP5TP7
Pz P2 P4 P6 P8P1P3P5P7
POz PO4 PO6 PO8
M2
PO3PO5PO7
M1
Oz O2O1
CB2CB1
AF3 AF4
Figure 1 Electrode placement in accordance with 64CH interna-tional 10ndash20 system
12 13 15 16 18 19 200 1 3 4 6 7 9 10
S1
S2
S3
(s)
Figure 2 Timing chart of the stimuli in one session
process risk and objective of the experiment The entireexperimental process was conducted under the experimentalethics and experimental safety regulations of the Chung-Ang University The subjects wore EEG caps on their headsand sat in a comfortable position in front of the monitorElectrodes were placed in accordance with the international10ndash20 system [26] The sampling rate of the EEG signals was250Hz and we used a band-pass filter to obtain signals from8 to 70Hz Commonly a low-frequency signal below 8Hzcontains biological noise such as blinking eyes and heartbeatThe following Figure 1 shows the location of the electrodesThe actual electrodes were placed on the human scalp so thatthe real position of the electrodes represented an arc In thisstudy however we installed the electrodes in a line for easein understanding the position of the electrodes
The subjects received three types of stimuli without feed-back To reduce noise the subjectmovements were restrictedThe experiment was repeated 10 times per subject Oneexperiment involved six sessions and one session consistedof seven epochs The order of the epochs was randomThe same epoch was repeated 60 times and each stimuluswas repeatedly administered 240 times The nonstationary
Journal of Applied Mathematics 5
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Subject 1
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
Figure 3 Results of grouping for Subject 1
characteristic of the EEG signals allowed us to obtain resultsfrom a number of trials despite the small number of subjects
We used three types of stimuli in this study audiovisual and finger movement Each stimulus was given in1 s A monitor an earphone and a button pad were usedto introduce the stimuli which were given in rotation Thetiming of the stimuli in one session is presented in thefollowing timing chart Figure 2
S1 S2 and S3 represent the three stimuli and their orderwas randomly changedThe resting time between stimuli was2 s and the entire session lasted for 20 s First one stimuluswas administered three times Subsequently two stimuli wereconcurrently administered At the final three stimuli weregiven at once At 300ms before the motion stimulus wasgiven a white cross mark appeared on the black screen
The audio stimulus was a 1000-Hz stereo pure tone madeby Stim2 The monitor screen was black when the audiostimulus was administered with the earphone controlled bya specific amplifier for the least delay stimulus The visualstimulus was administered in 1 s when the monitor screenwas green and the other stimuli were controlled so that theunrelated channels were unaffectedThemotion stimulus wasquite different from the other stimuli because the subjectmade a physical movement by pressing the button pad usingtheir fingers The motion image was difficult to notice in theexperiments and affected the concentration of the subjectTherefore we used real movements in this stimulus eventhough noise from the movements was present To reducethe noise the subject used only one finger and kept the otherbody parts still The motion stimulus indicates a white cross
6 Journal of Applied Mathematics
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minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
Subject 2
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
s
Figure 4 Results of grouping for Subject 2
symbol on the screen to prepare the subject and a whitesentence ldquopress buttonrdquo appeared on the screen to signal tothe subject to push the button pad
41 Channel Grouping For the channel grouping we usedthe PSF HS algorithm and the MSE method for the objectivefunction In the previous research we used the binary HSand the correlated coefficient However the previous methodexhibited poor performance in terms of computational timeIn this study we used the proposed method to improve thespeed and accuracy The number of iterations of the HS andHMS used in this study was 5000 and 20 respectively Thenumber of trials for one epoch of the HS algorithm was 10We identified the groups by determining the number of timeseach channel was grouped with the other channels All 30experiments for the three subjects were summarized in threeresults in Section 51
42 Feature Classification In the classification we used theHS algorithm with PSF where the HMS was 20 and thenumber of iterations was 50000 The results of channelgrouping which dimension of the signal data decreased wereused for classification The CSP algorithm was used forfeature extraction To match the number of channels we
selected a minimum number of channels among the stimuliIn the CSP procedure we used the data from the given twostimuli and determined the eigenvectors of the filtered signalThe data were classified without using any training method
5 Results and Analysis
51 Channel Grouping The results of the channel groupingare shown in the following Figures 3 4 and 5 and Table 1We searched the group by comparing all cases The resultsfor subject 1 exhibited were distinct The audio stimulusactivated the front of the brain the visual stimulus activatedthe backside of the brain and the motion stimulus activatedthe middle of the brain Generally the functions of the brainare known to be specialized by the locationThe area startingwith ldquoCrdquo includes the vicinity from the Cz channel to theC6 channel which is also known as the motor cortex Thevisual stimulus activated the visual cortex and the back ofthe brain A similar tendency could be observed in the othersubjects All subjects showed the relationship between themotion stimulus and the ldquoCrdquo area Although slight differencesexisted the results agreed with those of the existing researchwhich also explained the visual and audio stimuli Therefore
Journal of Applied Mathematics 7
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
Subject 3
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
Figure 5 Results of grouping for Subject 3
Table 1 Channel group results
Subject Stimuli Channel name
Subject 1Audio FP1 AF3 FT7 FC5 FC3 FC1 FCzVisual P5 P3 P1 Pz P2 P4 PO4Motions C5 C3 C1 Cz C2
Subject 2Audio P5 P3 P1 PzVisual PO7 PO5 PO3 POz O1Motions C5 C3 C1 Cz C2 CP2
Subject 3Audio F7 F5 F3 F1 Fz F2 F4 AF4Visual PO3 POz PO4 PO6 PO8 O1 O2 CB1Motions C5 C3 C1 Cz
we conclude that the results of the grouping are related to thegiven stimuli
Furthermore the similar groupmade by a single stimulusalso could be found in the groups subjected to two or threestimuli including a previous single stimulus Therefore wecan analyze the EEG and the brain by separated stimuliAdditionally the number of channels used in the analysis wasreduced by the proposed method These characteristics canmake the EEG analysis easier
However in some cases determining the relationshipbetween the stimulus and the group was difficult Thisproblem implied two limitations of this work First is thedifficulty in strictly controlling the experimental conditionsincluding the state of the subject A human brain constantlyand unconsciously receives stimuli If an unwanted stimulusis administered the accuracy would worsen The second isthe problem resulting from averaging the signals EEG signalshave a high temporal resolution and the data sampling rateused in this studywas 250HzHowever the groupingmethod
8 Journal of Applied Mathematics
Audio-visual
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 6 Classification accuracy of the audio and visual stimuli
Visual-motion
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 7 Classification accuracy of the visual and motion stimuli
focused on the summation of the MSEs in 1 s Therefore theadvantage of EEG was not well presented
52 Feature Classification We used the HS algorithm forclassification The classification in the feature space wasattempted 10 times for 120 features Figures 6 7 and 8 showthe results of the classification for two stimuli
For Subject 1 the best classification result was 7417and the worst classification result was 55 The averageaccuracy rate was 6283 For Subject 2 the best classificationresult was 7750 and the worst classification result was55 The average accuracy rate was 6461 For Subject3 the best classification result was 7250 and the worstclassification result was 5083 The average accuracy ratewas 6253 The HS algorithm is a heuristic algorithm thusthe results had an accuracy-rate range However without anylearning method or prior information the proposed method
Audio-motion
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 8 Classification accuracy of the audio and motion stimuli
exceeded the decision boundary of the two classes by a rateof approximately 63 Furthermore the classified data werenonlinear high-dimensional data
6 Conclusion
In this study we have built a group to easily analyze EEG sig-nals and proposed an EEG signal classificationmethod basedon HS Signal processing and classification are important asthey determine the accuracy of the entire system Thereforewe focused on solving the problems of a BCI system insignal processing and classification First we employed EEGsignal grouping using theHS algorithm to reduce the channelcomplexity From the EEG signal grouping we did not onlyreduce the channel used in the analysis but also separatelyanalyzed the signal of each stimulus Therefore the compu-tational time and the complexity of the data were reducedThe proposedmethod could be the smartest solution for EEGsignal analysis for problems with large data and dimensionNext we classified the EEG signals using HS We proposed anovel classification method with a metaheuristic algorithmfor nonlinear data such as the EEG signals The proposedmethod also focused on unsupervised classification thus wecould classify the data without training or prior informationWe believe that the proposed two methods could be helpfulin enhancing the BCI implementation and the EEG analysisfield
Acknowledgment
This work was supported by the Mid-Career ResearcherProgram through an NRF Grant funded by the MEST (no2012-0008726)
Journal of Applied Mathematics 9
References
[1] L Bi X Fan and Y Liu ldquoEEG-based brain-controlled mobilerobots a surveyrdquo IEEE Transactions on Human-Machine Sys-tems vol 43 no 2 pp 161ndash176 2013
[2] I Iturrate J M Antelis A Kubler and J Minguez ldquoA nonin-vasive brain-actuated wheelchair based on a P300 neurophysi-ological protocol and automated navigationrdquo IEEE Transactionson Robotics vol 25 no 3 pp 614ndash627 2009
[3] A Frisoli C Loconsole D Leonardis et al ldquoA new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitationin real-world tasksrdquo IEEE Transactions on Systems Man andCybernetics C vol 42 no 6 pp 1169ndash1179 2012
[4] J R Wolpaw ldquoBrain-computer interface research comes of agetraditional assumptions meet emerging realitiesrdquo Journal ofMotor Behavior vol 42 no 6 pp 351ndash353 2010
[5] E E Fetz ldquoReal-time control of a robotic arm by neuronalensemblesrdquoNature Neuroscience vol 2 no 7 pp 583ndash584 1999
[6] P LeVan J Maclaren M Herbst R Sostheim M Zaitsev andJ Hennig ldquoBallistocardiographic artifact removal from simul-taneous EEG-fMRI using an optical motion-tracking systemrdquoNeuroImage vol 75 pp 1ndash11 2013
[7] A Ishikawa H Udagawa YMasuda S Kohno T Amita and YInoue ldquoDevelopment of double density whole brain fNIRSwithEEG system for brain machine interfacerdquo in Proceedings of the33rd Annual International Conference of the IEEE Engineeringin Medicine and Biology Society (EMBS rsquo11) pp 6118ndash6122September 2011
[8] O A P Sosa Y Quijano M Doniz and J E Chong-QueroldquoBCI a historical analysis and technology comparisonrdquo in Pro-ceedings of the Pan American Health Care Exchanges Conference(PAHCE rsquo11) pp 205ndash209 Rio de Janeiro Brazil April 2011
[9] Y O Halchenko S J Hanson and B A Pearlmutter ldquoMulti-modal integration FMRIMRI EEGMEGrdquo inAdvanced ImageProcessing in Magnetic Resonance Imaging CRC Press BocaRaton Fla USA 2005
[10] T N Lal M Schroder T Hinterberger et al ldquoSupport vectorchannel selection in BCIrdquo IEEE Transactions on BiomedicalEngineering vol 51 no 6 pp 1003ndash1010 2004
[11] J Meng G Huang D Zhang and X Zhu ldquoOptimizing spatialspectral patterns jointly with channel configuration for brain-computer interfacerdquoNeurocomputing vol 104 pp 115ndash126 2013
[12] C Brunner M Naeem and G Pfurtscheller ldquoDimensionalityreduction and channel selection of motor imagery electroen-cephalographic datardquo Computational Intelligence and Neuro-science vol 2009 Article ID 537504 8 pages 2009
[13] H Lu H-L Eng C Guan K N Plataniotis and A NVenetsanopoulos ldquoRegularized common spatial pattern withaggregation for EEG Classification in small-sample settingrdquoIEEE Transactions on Biomedical Engineering vol 57 no 12 pp2936ndash2946 2010
[14] B Wang C M Wong F Wan P U Mak P-I Mak and M IVai ldquoTrial pruning based on genetic algorithm for single-trialEEG classificationrdquo Computers and Electrical Engineering vol38 no 1 pp 35ndash44 2012
[15] B Blankertz S Lemm M Treder S Haufe and K-R MullerldquoSingle-trial analysis and classification of ERP components atutorialrdquo NeuroImage vol 56 no 2 pp 814ndash825 2011
[16] S Siuly and Y Li ldquoImproving the separability of motor imageryEEG signals using a cross correlation-based least square support
vector machine for brain-computer interfacerdquo IEEE Transac-tions on Neural Systems and Rehabilitation Engineering vol 20no 4 pp 526ndash538 2012
[17] A Vuckovic and F Sepulveda ldquoDelta band contribution incue based single trial classification of real and imaginary wristmovementsrdquo Medical and Biological Engineering and Comput-ing vol 46 no 6 pp 529ndash539 2008
[18] K S Lee and Z W Geem ldquoA new meta-heuristic algorithm forcontinuous engineering optimization harmony search theoryand practicerdquo Computer Methods in Applied Mechanics andEngineering vol 194 no 36ndash38 pp 3902ndash3933 2005
[19] X-S Yang ldquoHarmony search as a metaheuristic algorithmrdquoMusic-Inspired Harmony Search Algorithm vol 191 pp 1ndash142009
[20] Z W Geem J H Kim and G V Loganathan ldquoA new heuristicoptimization algorithm harmony searchrdquo Simulation vol 76no 2 pp 60ndash68 2001
[21] Q-K Pan P N Suganthan M F Tasgetiren and J J LiangldquoA self-adaptive global best harmony search algorithm forcontinuous optimization problemsrdquo Applied Mathematics andComputation vol 216 no 3 pp 830ndash848 2010
[22] L D S Coelho and V C Mariani ldquoAn improved harmonysearch algorithm for power economic load dispatchrdquo EnergyConversion and Management vol 50 no 10 pp 2522ndash25262009
[23] Z W Geem and K-B Sim ldquoParameter-setting-free harmonysearch algorithmrdquo Applied Mathematics and Computation vol217 no 8 pp 3881ndash3889 2010
[24] M G Wentrup and M Buss ldquoMulticlass common spatialpatterns and information theoretic feature extractionrdquo IEEETransactions on Biomedical Engineering vol 55 no 8 pp 1991ndash2000 2008
[25] L Wang Y Mao Q Niu and M Fei ldquoA multi-objective binaryharmony search algorithmrdquoAdvances in Swarm Intelligence vol6729 no 2 pp 74ndash81 2011
[26] E Niedermeyer and F L da Silva ldquoEEG recording andoperation of the apparatusrdquo in Electroencephalography BasicPrinciples Clinical Applications and Related Fields LippincottWilliams and Wilkins Baltimore Md USA 2004
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Decision SciencesAdvances in
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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
2 Journal of Applied Mathematics
a larger space for the equipment and building the entiresystem is expensive [8] Further the multimodal experi-mental method can be potentially contaminated [9] On theother hand the channel-selection method is a preferableand convenient method although some data are droppedThis method restricts the channels using the brain area ormathematical models [10ndash12] However this method focusesonly on one stimulus at a time Therefore it cannot handlesimultaneous stimuli in the human brain The BCI systemthat employs the EEG signal for actual use overcomes thedisadvantage of the curse of dimension
In the BCI system the performance is dependent onfeature extraction and classification From the raw-datasignal we can extract the EEG features In this process thecommon spatial pattern (CSP) algorithm is commonly usedas a feature extraction algorithm [13 14] In the classificationeven though many approaches have been attempted and thesupport vector machine (SVM) is preferred [15ndash17] a dom-inant algorithm is absent The linear discriminant analysisand the original SVM algorithm are linear classificationsand their performance in nonlinear EEG signal analysis ispoor The SVM algorithm with a kernel function is not onlyredundant when the dimension becomes higher but alsodifficult in unsupervised cases The artificial neural networkis powerful and can classify nonlinear data however it suffersfrom a weakness because the length of time needed forlearning cannot be approximated Thus a new approach toBCI feature classification is needed
The present paper presents a solution to the two problemsof conventional channel selection and feature classificationusing the harmony search (HS) algorithm a music-inspiredmetaheuristic algorithm The metaheuristic algorithm findsthe solution using rules and randomness thus it does notrequire too many computational resources [18] In non-deterministic polynomial-time hard (NP-hard) problems wecan solve the problem using the heuristic algorithm fasterthan that using the exact computational method The HSalgorithm one of the nature-inspired heuristic algorithmspossesses advantages that are not available in the otherheuristic algorithms HS can maintain diversity thus thesearch solution effectively and easily escapes from the localoptimum [19] In addition the HS algorithm is easy to usehas a simple structure and requires only two parameters Byusing the HS algorithm we can determine the group of EEGsignal channels to eliminate the restriction of the curse ofdimension in signal processing and develop a classificationfor BCI system applications In Section 2 we present thetheoretical background of the HS and feature extractionalgorithms In Section 3 we show the proposed methodsSections 4 and 5 present the experiment and analysis of thechannel grouping and classification We conclude this paperin Section 6
2 Theoretical Background
21 Harmony Search Algorithm As the name suggests theHS algorithm originates from musical harmony [20] If weassume that the variables are the instruments the pitches of
the instruments are the values of the variables Similar tothe orchestra player who determines the harmony to createa good balance the HS algorithm searches the best stateof a given problem defined by an objective function TheHS algorithm has a harmony memory (HM) that saves theprevious results and its structure is described in a matrixform
[[[[[[
11990911
11990912
1199091119873
11990921
11990922
1199092119873
119909HMS1
119909HMS2
119909HMS119873
]]]]]]
(1)
where 119909119894119899is the selected solution of the 119899th variable HMS
is the HM size and 119873 is the number of variables The HMobtains the latest best result thus the HM members changewhen the result of the new value is better than the worstresult in the HM The new value is selected in three waysselected byHM randomly selected from the range of possiblesolutions or selected by a local area search called the pitchadjustment The selection by the HM is performed usingthe HM consideration rate (HMCR) The pitch adjustmenta mutation of the HM member also has an occurrencerate which is called the pitch adjustment rate (PAR) Therandom search and the pitch adjustment prevent prematureconvergence andmaintain the diversity of the solutionTheseprocesses are represented as follows
119909New119899
larr997888
119909119899isin 119883119899 119883 is possible solution wp 119877Random
119909119899isin 1199091119899 1199092119899 119909HMS
119899 wp 119877Memory
119909119894119899plusmn 119898 wp 119877pitch
(2)where 119898 is the pitch adjustment size and 119877Random 119877Memoryand 119877pitch are the probability of the random search HMCRand PAR respectively
22 Parameter-Setting-Free Harmony Search In the basicHS algorithm principle we use several parameters namelyHMS HMCR and PAR Generally the HMS value rangesfrom 10 to 100 Although this value depends on the iterationnumber the result is not sensitive to the HMS In contrastthe HMCR and PAR control the result of the HS algorithmTo select a proper parameter value some investigations havebeen conducted [21 22] The parameter-setting-free (PSF)HS proposed by Geem and Sim [23] is one of the parametercontrol methods for automatic selection of the parametersThis method uses onemorememory to represent themethodthat generates theHMvaluesThememory is the same as thatof the HMThen we obtain the new parameter values on thebasis of the following equations
HMCR119899= Number of 119909
119899made by ldquorandom selectionrdquo
HMS
PAR119899= Number of 119909
119899made by ldquopitch adjustmentrdquo
HMS(3)
Journal of Applied Mathematics 3
In each iteration time the PSF HS calculates the HMCRand PAR Then we obtain the proper adaptive parametersUsing this PSF technique the value to be initialized is onlythe HMS
23 Common Spatial Patterns The CSP algorithm is one ofthe useful methods for feature extraction of the EEG signalsFor two different classes 119888
1and 1198882 we obtain multiple spatial
filters that maximize the variance of one class and minimizethat of the other class The spatial filter matrix119882 is obtainedby solving the following equation [24]
wlowast = arg maxwisin119877119873
wT119877s|1198881wwT119877s|1198882w
(4)
where 119877s|1198881 and 119877s|1198882 are the covariance matrices of signal sgiven in each class The 119882 matrix consists of 119871 eigenvectorsof wlowast Thus the final goal of the CSP is to obtain the lineartransform s which is represented by the following equation
s = 119882Ts (5)
3 Methodology
31 Mean Square Error and Euclidean Matrices To derivethe objective function the mean square error (MSE) andthe Euclidean distances were used The MSE matrix wasused for grouping the EEG channels A low MSE means thatthe two signals are almost the same Moreover if the MSEis zero the two signals are the same Therefore the groupwith a minimum MSE has similar characteristics and can beregarded to have the same reaction for the same stimulus Forthe grouping of the signals we first normalized the EEG dataacquired from 64 channels We divided the EEG signals withthe maximum values in each channel Then the elementsin the 119895th row and 119896th column of the MSE matrix wererepresented as follows (here the diagonal members of theMSE matrix were zero)
MSE119895119896
= 1Tsum119905
119904119895(119905) minus 119904
119896(119905)2 where 119895 119896 = 1 2 64
(6)
In (6) 119904119895(119905) and 119904
119896(119905) are the normalized signal vectors of
the119895th and 119896th channels at time 119905 respectivelyFor the feature extraction we used the Euclidean distance
matrix Similar to grouping we assumed that the sameclass members were located close to one another Fromthis assumption we determined the group with the shortestoverall distance After feature extraction was completed thefeatures were expressed in a vector space Then the (119895 119896)elements of the Euclidean distance matrix were representedas follows (the diagonal of the MSE matrix is zero and 119904lowastrepresents the mean feature vector in the 119871-119863 space)
Dist119895119896
= 1003816100381610038161003816100381610038161003816100381610038161003816119904lowast
119895minus 119904lowast119896
10038161003816100381610038161003816100381610038161003816100381610038162 where 119895 119896 = 1 2 119871 (7)
32 Minimum-Seeking Harmony Search To represent themember of a group or class we used a binary switch vector
(BSV) which indicates which channel or vector was selectedIf the 119895thmember of the BSV is one the 119895th channel or vectoris included in the group or class Therefore we determine theBSV values that generate the minimum result of the objectivefunction using the HS algorithmThe objective function usedin this study is the following
arg min119863119904lowastℎ
1 + 119863119904lowastℎ
(8)
119863 = 12sum119895
sum119896
119889119895119896 (9)
In (8) 119904lowast is the channel number or feature selected by theBSV and ℎ is a constant determined by 119904lowast The numeral ldquo1rdquoin the numerator is used to prevent the value of (8) frombecoming zero 119863 is the summation of the entire MSE orthe distances selected by the BSV In (9) 119889
119895119896is the selected
member of the MSE matrix or the Euclidean distance matrixselected by the BSV
BSV contains the information in which the data espe-cially the channels or features are selected To apply the BSVto the HS algorithm we can use the binary HS algorithm[25] However the binary HS algorithm has difficulty inapplying pitch adjustment and requires more memory thanthe basic HS algorithm This characteristic affects the speedperformance and accuracy of the binary HS algorithmThusin the HS we converted each 119903 binary number in the BSV toa 2119903-radixThen the solution ranged from 0 to 2119903 minus1 and thepitch adjustment size 119898 was 119903 In this study we used 119903 = 4When the value was above the range of the pitch adjustmentwe used the circular method
The value of ℎ was determined by the following processwhen the value of 119904lowast is from 0 to 119878lowast the objective function isrepresented as follows
Objective function =
10ℎ = infin if 119904lowast = 01 + 01ℎ = 1 if 119904lowast = 1
1 + 119863119904lowastℎ
if 1 lt 119904lowast lt 119878lowast(10)
We assumed that the optimal value of 119904lowast was 119904 and thevalue of the objective function with 119904 was represented as 119891
119904
Then if 119904lowast = 119904 minus 1 the objective function satisfies thefollowing equation
1 + 119863119904minus1
(119904 minus 1)ℎ=1 + 119863
119904minus sum119895119889119895119896
119904ℎ ( 119904119904 minus 1)
ℎ
= (119891119904minussum119895119889119895119896
119904ℎ )(1 + 1119904 minus 1)
ℎ
(11)
From the definition of 119904 the following relationship shouldbe satisfied
1003816100381610038161003816119891119904minus1 minus 119891119904
1003816100381610038161003816 gt 0 (12)
4 Journal of Applied Mathematics
If 119904lowast = 119904 + 1 the objective function satisfies the followingequation
1 + 119863119904+1
(119904 + 1)ℎ=1 + 119863
119904+ sum119895119889119895119896
119904ℎ ( 119904119904 + 1)
ℎ
= (119891119904+sum119895119889119895119896
119904ℎ )(1 minus 1119904 + 1)
ℎ
(13)
From the definition of 119904 the following relationship shouldalso be satisfied
1003816100381610038161003816119891119904+1 minus 119891119904
1003816100381610038161003816 gt 0 (14)
From (11) and (12) we obtain the following relationship
119891119904gt (1 + (1119904 minus 1))ℎ (sum
119895119889119895119896119904)
(1 + (1119904 minus 1))ℎ minus 1 (15)
From (10) 119891119904le 1 Then (15) is transformed into the
following relationship
119904ℎ minus (119904 minus 1)ℎ gt sum119895
119889119895119896 (16)
The same process can be applied to (13) and (14) From(13) (14) and (16) we obtain the following
119878lowast
sum119895=1
119889119895119896+ (119904 minus 1)ℎ lt sℎ lt
119878lowast+1
sum119895=1
119889119895119896+ (119904 + 1)ℎ (17)
Equation (17) should be satisfied for all 119896 values There-foresum119878lowast
119895=1119889119895119896andsum119878lowast+1
119895=1119889119895119896can be respectively converted to
the maximum summation of 119889119895119896 which has a value of one
in the BSV (119889maxone) and the minimum summation of 119889119895119896
which has a value of zero in the BSV (119889minzero) Equation (17)is rewritten as follows
119889maxone + (119904 minus 1)ℎ lt sℎ lt 119889minzero + (119904 + 1)ℎ (18)
In this equation 119889maxone and 119889minzero are constant and 119904is determined by the HS process Therefore the value of ℎshould satisfy (18)
4 Experiment
In the experiments we used a commercial product theNeuroscan EEG signal acquisition device (Neuroscan systemCompumedics USA) to acquire the EEG signalsThis deviceconsists of the Synamps2 and Stim2 hardware and thesoftware for acquisition and preprocessing is Curry 7 Weused an EEG cap with 64 electrodes for the EEG signals andfive additional electrodes for noise reduction The subjectswere three healthy men in their mid-twenties The subjectswere selected without using any selection criteria to ensuregenerality of the results All subjects had undergone oneor more brain-wave experiments not related to this studyThe subjects were fully aware of the entire experimental
FP1 FP2FPz
Fz F2 F4 F6 F8F1F3F5F7
FCz FC2 FC4 FC6 FT8FC1FC3FC5FT7
Cz C2 C4 C6 T8C1C3C5T7
CPz CP2 CP4 CP6 TP8CP1CP3CP5TP7
Pz P2 P4 P6 P8P1P3P5P7
POz PO4 PO6 PO8
M2
PO3PO5PO7
M1
Oz O2O1
CB2CB1
AF3 AF4
Figure 1 Electrode placement in accordance with 64CH interna-tional 10ndash20 system
12 13 15 16 18 19 200 1 3 4 6 7 9 10
S1
S2
S3
(s)
Figure 2 Timing chart of the stimuli in one session
process risk and objective of the experiment The entireexperimental process was conducted under the experimentalethics and experimental safety regulations of the Chung-Ang University The subjects wore EEG caps on their headsand sat in a comfortable position in front of the monitorElectrodes were placed in accordance with the international10ndash20 system [26] The sampling rate of the EEG signals was250Hz and we used a band-pass filter to obtain signals from8 to 70Hz Commonly a low-frequency signal below 8Hzcontains biological noise such as blinking eyes and heartbeatThe following Figure 1 shows the location of the electrodesThe actual electrodes were placed on the human scalp so thatthe real position of the electrodes represented an arc In thisstudy however we installed the electrodes in a line for easein understanding the position of the electrodes
The subjects received three types of stimuli without feed-back To reduce noise the subjectmovements were restrictedThe experiment was repeated 10 times per subject Oneexperiment involved six sessions and one session consistedof seven epochs The order of the epochs was randomThe same epoch was repeated 60 times and each stimuluswas repeatedly administered 240 times The nonstationary
Journal of Applied Mathematics 5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
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0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
Subject 1
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
Figure 3 Results of grouping for Subject 1
characteristic of the EEG signals allowed us to obtain resultsfrom a number of trials despite the small number of subjects
We used three types of stimuli in this study audiovisual and finger movement Each stimulus was given in1 s A monitor an earphone and a button pad were usedto introduce the stimuli which were given in rotation Thetiming of the stimuli in one session is presented in thefollowing timing chart Figure 2
S1 S2 and S3 represent the three stimuli and their orderwas randomly changedThe resting time between stimuli was2 s and the entire session lasted for 20 s First one stimuluswas administered three times Subsequently two stimuli wereconcurrently administered At the final three stimuli weregiven at once At 300ms before the motion stimulus wasgiven a white cross mark appeared on the black screen
The audio stimulus was a 1000-Hz stereo pure tone madeby Stim2 The monitor screen was black when the audiostimulus was administered with the earphone controlled bya specific amplifier for the least delay stimulus The visualstimulus was administered in 1 s when the monitor screenwas green and the other stimuli were controlled so that theunrelated channels were unaffectedThemotion stimulus wasquite different from the other stimuli because the subjectmade a physical movement by pressing the button pad usingtheir fingers The motion image was difficult to notice in theexperiments and affected the concentration of the subjectTherefore we used real movements in this stimulus eventhough noise from the movements was present To reducethe noise the subject used only one finger and kept the otherbody parts still The motion stimulus indicates a white cross
6 Journal of Applied Mathematics
minus5 0 5minus5
0
5
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5
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5
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5
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5
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0
5
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0
5
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0
5
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0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
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0
5
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0
5
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0
5
minus5 0 5minus5
0
5
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0
5
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0
5
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0
5
minus5 0 5minus5
0
5
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0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
Subject 2
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
s
Figure 4 Results of grouping for Subject 2
symbol on the screen to prepare the subject and a whitesentence ldquopress buttonrdquo appeared on the screen to signal tothe subject to push the button pad
41 Channel Grouping For the channel grouping we usedthe PSF HS algorithm and the MSE method for the objectivefunction In the previous research we used the binary HSand the correlated coefficient However the previous methodexhibited poor performance in terms of computational timeIn this study we used the proposed method to improve thespeed and accuracy The number of iterations of the HS andHMS used in this study was 5000 and 20 respectively Thenumber of trials for one epoch of the HS algorithm was 10We identified the groups by determining the number of timeseach channel was grouped with the other channels All 30experiments for the three subjects were summarized in threeresults in Section 51
42 Feature Classification In the classification we used theHS algorithm with PSF where the HMS was 20 and thenumber of iterations was 50000 The results of channelgrouping which dimension of the signal data decreased wereused for classification The CSP algorithm was used forfeature extraction To match the number of channels we
selected a minimum number of channels among the stimuliIn the CSP procedure we used the data from the given twostimuli and determined the eigenvectors of the filtered signalThe data were classified without using any training method
5 Results and Analysis
51 Channel Grouping The results of the channel groupingare shown in the following Figures 3 4 and 5 and Table 1We searched the group by comparing all cases The resultsfor subject 1 exhibited were distinct The audio stimulusactivated the front of the brain the visual stimulus activatedthe backside of the brain and the motion stimulus activatedthe middle of the brain Generally the functions of the brainare known to be specialized by the locationThe area startingwith ldquoCrdquo includes the vicinity from the Cz channel to theC6 channel which is also known as the motor cortex Thevisual stimulus activated the visual cortex and the back ofthe brain A similar tendency could be observed in the othersubjects All subjects showed the relationship between themotion stimulus and the ldquoCrdquo area Although slight differencesexisted the results agreed with those of the existing researchwhich also explained the visual and audio stimuli Therefore
Journal of Applied Mathematics 7
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
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0
5
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0
5
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0
5
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0
5
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0
5
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0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
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0
5
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0
5
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0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
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0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
Subject 3
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
Figure 5 Results of grouping for Subject 3
Table 1 Channel group results
Subject Stimuli Channel name
Subject 1Audio FP1 AF3 FT7 FC5 FC3 FC1 FCzVisual P5 P3 P1 Pz P2 P4 PO4Motions C5 C3 C1 Cz C2
Subject 2Audio P5 P3 P1 PzVisual PO7 PO5 PO3 POz O1Motions C5 C3 C1 Cz C2 CP2
Subject 3Audio F7 F5 F3 F1 Fz F2 F4 AF4Visual PO3 POz PO4 PO6 PO8 O1 O2 CB1Motions C5 C3 C1 Cz
we conclude that the results of the grouping are related to thegiven stimuli
Furthermore the similar groupmade by a single stimulusalso could be found in the groups subjected to two or threestimuli including a previous single stimulus Therefore wecan analyze the EEG and the brain by separated stimuliAdditionally the number of channels used in the analysis wasreduced by the proposed method These characteristics canmake the EEG analysis easier
However in some cases determining the relationshipbetween the stimulus and the group was difficult Thisproblem implied two limitations of this work First is thedifficulty in strictly controlling the experimental conditionsincluding the state of the subject A human brain constantlyand unconsciously receives stimuli If an unwanted stimulusis administered the accuracy would worsen The second isthe problem resulting from averaging the signals EEG signalshave a high temporal resolution and the data sampling rateused in this studywas 250HzHowever the groupingmethod
8 Journal of Applied Mathematics
Audio-visual
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 6 Classification accuracy of the audio and visual stimuli
Visual-motion
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 7 Classification accuracy of the visual and motion stimuli
focused on the summation of the MSEs in 1 s Therefore theadvantage of EEG was not well presented
52 Feature Classification We used the HS algorithm forclassification The classification in the feature space wasattempted 10 times for 120 features Figures 6 7 and 8 showthe results of the classification for two stimuli
For Subject 1 the best classification result was 7417and the worst classification result was 55 The averageaccuracy rate was 6283 For Subject 2 the best classificationresult was 7750 and the worst classification result was55 The average accuracy rate was 6461 For Subject3 the best classification result was 7250 and the worstclassification result was 5083 The average accuracy ratewas 6253 The HS algorithm is a heuristic algorithm thusthe results had an accuracy-rate range However without anylearning method or prior information the proposed method
Audio-motion
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 8 Classification accuracy of the audio and motion stimuli
exceeded the decision boundary of the two classes by a rateof approximately 63 Furthermore the classified data werenonlinear high-dimensional data
6 Conclusion
In this study we have built a group to easily analyze EEG sig-nals and proposed an EEG signal classificationmethod basedon HS Signal processing and classification are important asthey determine the accuracy of the entire system Thereforewe focused on solving the problems of a BCI system insignal processing and classification First we employed EEGsignal grouping using theHS algorithm to reduce the channelcomplexity From the EEG signal grouping we did not onlyreduce the channel used in the analysis but also separatelyanalyzed the signal of each stimulus Therefore the compu-tational time and the complexity of the data were reducedThe proposedmethod could be the smartest solution for EEGsignal analysis for problems with large data and dimensionNext we classified the EEG signals using HS We proposed anovel classification method with a metaheuristic algorithmfor nonlinear data such as the EEG signals The proposedmethod also focused on unsupervised classification thus wecould classify the data without training or prior informationWe believe that the proposed two methods could be helpfulin enhancing the BCI implementation and the EEG analysisfield
Acknowledgment
This work was supported by the Mid-Career ResearcherProgram through an NRF Grant funded by the MEST (no2012-0008726)
Journal of Applied Mathematics 9
References
[1] L Bi X Fan and Y Liu ldquoEEG-based brain-controlled mobilerobots a surveyrdquo IEEE Transactions on Human-Machine Sys-tems vol 43 no 2 pp 161ndash176 2013
[2] I Iturrate J M Antelis A Kubler and J Minguez ldquoA nonin-vasive brain-actuated wheelchair based on a P300 neurophysi-ological protocol and automated navigationrdquo IEEE Transactionson Robotics vol 25 no 3 pp 614ndash627 2009
[3] A Frisoli C Loconsole D Leonardis et al ldquoA new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitationin real-world tasksrdquo IEEE Transactions on Systems Man andCybernetics C vol 42 no 6 pp 1169ndash1179 2012
[4] J R Wolpaw ldquoBrain-computer interface research comes of agetraditional assumptions meet emerging realitiesrdquo Journal ofMotor Behavior vol 42 no 6 pp 351ndash353 2010
[5] E E Fetz ldquoReal-time control of a robotic arm by neuronalensemblesrdquoNature Neuroscience vol 2 no 7 pp 583ndash584 1999
[6] P LeVan J Maclaren M Herbst R Sostheim M Zaitsev andJ Hennig ldquoBallistocardiographic artifact removal from simul-taneous EEG-fMRI using an optical motion-tracking systemrdquoNeuroImage vol 75 pp 1ndash11 2013
[7] A Ishikawa H Udagawa YMasuda S Kohno T Amita and YInoue ldquoDevelopment of double density whole brain fNIRSwithEEG system for brain machine interfacerdquo in Proceedings of the33rd Annual International Conference of the IEEE Engineeringin Medicine and Biology Society (EMBS rsquo11) pp 6118ndash6122September 2011
[8] O A P Sosa Y Quijano M Doniz and J E Chong-QueroldquoBCI a historical analysis and technology comparisonrdquo in Pro-ceedings of the Pan American Health Care Exchanges Conference(PAHCE rsquo11) pp 205ndash209 Rio de Janeiro Brazil April 2011
[9] Y O Halchenko S J Hanson and B A Pearlmutter ldquoMulti-modal integration FMRIMRI EEGMEGrdquo inAdvanced ImageProcessing in Magnetic Resonance Imaging CRC Press BocaRaton Fla USA 2005
[10] T N Lal M Schroder T Hinterberger et al ldquoSupport vectorchannel selection in BCIrdquo IEEE Transactions on BiomedicalEngineering vol 51 no 6 pp 1003ndash1010 2004
[11] J Meng G Huang D Zhang and X Zhu ldquoOptimizing spatialspectral patterns jointly with channel configuration for brain-computer interfacerdquoNeurocomputing vol 104 pp 115ndash126 2013
[12] C Brunner M Naeem and G Pfurtscheller ldquoDimensionalityreduction and channel selection of motor imagery electroen-cephalographic datardquo Computational Intelligence and Neuro-science vol 2009 Article ID 537504 8 pages 2009
[13] H Lu H-L Eng C Guan K N Plataniotis and A NVenetsanopoulos ldquoRegularized common spatial pattern withaggregation for EEG Classification in small-sample settingrdquoIEEE Transactions on Biomedical Engineering vol 57 no 12 pp2936ndash2946 2010
[14] B Wang C M Wong F Wan P U Mak P-I Mak and M IVai ldquoTrial pruning based on genetic algorithm for single-trialEEG classificationrdquo Computers and Electrical Engineering vol38 no 1 pp 35ndash44 2012
[15] B Blankertz S Lemm M Treder S Haufe and K-R MullerldquoSingle-trial analysis and classification of ERP components atutorialrdquo NeuroImage vol 56 no 2 pp 814ndash825 2011
[16] S Siuly and Y Li ldquoImproving the separability of motor imageryEEG signals using a cross correlation-based least square support
vector machine for brain-computer interfacerdquo IEEE Transac-tions on Neural Systems and Rehabilitation Engineering vol 20no 4 pp 526ndash538 2012
[17] A Vuckovic and F Sepulveda ldquoDelta band contribution incue based single trial classification of real and imaginary wristmovementsrdquo Medical and Biological Engineering and Comput-ing vol 46 no 6 pp 529ndash539 2008
[18] K S Lee and Z W Geem ldquoA new meta-heuristic algorithm forcontinuous engineering optimization harmony search theoryand practicerdquo Computer Methods in Applied Mechanics andEngineering vol 194 no 36ndash38 pp 3902ndash3933 2005
[19] X-S Yang ldquoHarmony search as a metaheuristic algorithmrdquoMusic-Inspired Harmony Search Algorithm vol 191 pp 1ndash142009
[20] Z W Geem J H Kim and G V Loganathan ldquoA new heuristicoptimization algorithm harmony searchrdquo Simulation vol 76no 2 pp 60ndash68 2001
[21] Q-K Pan P N Suganthan M F Tasgetiren and J J LiangldquoA self-adaptive global best harmony search algorithm forcontinuous optimization problemsrdquo Applied Mathematics andComputation vol 216 no 3 pp 830ndash848 2010
[22] L D S Coelho and V C Mariani ldquoAn improved harmonysearch algorithm for power economic load dispatchrdquo EnergyConversion and Management vol 50 no 10 pp 2522ndash25262009
[23] Z W Geem and K-B Sim ldquoParameter-setting-free harmonysearch algorithmrdquo Applied Mathematics and Computation vol217 no 8 pp 3881ndash3889 2010
[24] M G Wentrup and M Buss ldquoMulticlass common spatialpatterns and information theoretic feature extractionrdquo IEEETransactions on Biomedical Engineering vol 55 no 8 pp 1991ndash2000 2008
[25] L Wang Y Mao Q Niu and M Fei ldquoA multi-objective binaryharmony search algorithmrdquoAdvances in Swarm Intelligence vol6729 no 2 pp 74ndash81 2011
[26] E Niedermeyer and F L da Silva ldquoEEG recording andoperation of the apparatusrdquo in Electroencephalography BasicPrinciples Clinical Applications and Related Fields LippincottWilliams and Wilkins Baltimore Md USA 2004
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Journal of Applied Mathematics 3
In each iteration time the PSF HS calculates the HMCRand PAR Then we obtain the proper adaptive parametersUsing this PSF technique the value to be initialized is onlythe HMS
23 Common Spatial Patterns The CSP algorithm is one ofthe useful methods for feature extraction of the EEG signalsFor two different classes 119888
1and 1198882 we obtain multiple spatial
filters that maximize the variance of one class and minimizethat of the other class The spatial filter matrix119882 is obtainedby solving the following equation [24]
wlowast = arg maxwisin119877119873
wT119877s|1198881wwT119877s|1198882w
(4)
where 119877s|1198881 and 119877s|1198882 are the covariance matrices of signal sgiven in each class The 119882 matrix consists of 119871 eigenvectorsof wlowast Thus the final goal of the CSP is to obtain the lineartransform s which is represented by the following equation
s = 119882Ts (5)
3 Methodology
31 Mean Square Error and Euclidean Matrices To derivethe objective function the mean square error (MSE) andthe Euclidean distances were used The MSE matrix wasused for grouping the EEG channels A low MSE means thatthe two signals are almost the same Moreover if the MSEis zero the two signals are the same Therefore the groupwith a minimum MSE has similar characteristics and can beregarded to have the same reaction for the same stimulus Forthe grouping of the signals we first normalized the EEG dataacquired from 64 channels We divided the EEG signals withthe maximum values in each channel Then the elementsin the 119895th row and 119896th column of the MSE matrix wererepresented as follows (here the diagonal members of theMSE matrix were zero)
MSE119895119896
= 1Tsum119905
119904119895(119905) minus 119904
119896(119905)2 where 119895 119896 = 1 2 64
(6)
In (6) 119904119895(119905) and 119904
119896(119905) are the normalized signal vectors of
the119895th and 119896th channels at time 119905 respectivelyFor the feature extraction we used the Euclidean distance
matrix Similar to grouping we assumed that the sameclass members were located close to one another Fromthis assumption we determined the group with the shortestoverall distance After feature extraction was completed thefeatures were expressed in a vector space Then the (119895 119896)elements of the Euclidean distance matrix were representedas follows (the diagonal of the MSE matrix is zero and 119904lowastrepresents the mean feature vector in the 119871-119863 space)
Dist119895119896
= 1003816100381610038161003816100381610038161003816100381610038161003816119904lowast
119895minus 119904lowast119896
10038161003816100381610038161003816100381610038161003816100381610038162 where 119895 119896 = 1 2 119871 (7)
32 Minimum-Seeking Harmony Search To represent themember of a group or class we used a binary switch vector
(BSV) which indicates which channel or vector was selectedIf the 119895thmember of the BSV is one the 119895th channel or vectoris included in the group or class Therefore we determine theBSV values that generate the minimum result of the objectivefunction using the HS algorithmThe objective function usedin this study is the following
arg min119863119904lowastℎ
1 + 119863119904lowastℎ
(8)
119863 = 12sum119895
sum119896
119889119895119896 (9)
In (8) 119904lowast is the channel number or feature selected by theBSV and ℎ is a constant determined by 119904lowast The numeral ldquo1rdquoin the numerator is used to prevent the value of (8) frombecoming zero 119863 is the summation of the entire MSE orthe distances selected by the BSV In (9) 119889
119895119896is the selected
member of the MSE matrix or the Euclidean distance matrixselected by the BSV
BSV contains the information in which the data espe-cially the channels or features are selected To apply the BSVto the HS algorithm we can use the binary HS algorithm[25] However the binary HS algorithm has difficulty inapplying pitch adjustment and requires more memory thanthe basic HS algorithm This characteristic affects the speedperformance and accuracy of the binary HS algorithmThusin the HS we converted each 119903 binary number in the BSV toa 2119903-radixThen the solution ranged from 0 to 2119903 minus1 and thepitch adjustment size 119898 was 119903 In this study we used 119903 = 4When the value was above the range of the pitch adjustmentwe used the circular method
The value of ℎ was determined by the following processwhen the value of 119904lowast is from 0 to 119878lowast the objective function isrepresented as follows
Objective function =
10ℎ = infin if 119904lowast = 01 + 01ℎ = 1 if 119904lowast = 1
1 + 119863119904lowastℎ
if 1 lt 119904lowast lt 119878lowast(10)
We assumed that the optimal value of 119904lowast was 119904 and thevalue of the objective function with 119904 was represented as 119891
119904
Then if 119904lowast = 119904 minus 1 the objective function satisfies thefollowing equation
1 + 119863119904minus1
(119904 minus 1)ℎ=1 + 119863
119904minus sum119895119889119895119896
119904ℎ ( 119904119904 minus 1)
ℎ
= (119891119904minussum119895119889119895119896
119904ℎ )(1 + 1119904 minus 1)
ℎ
(11)
From the definition of 119904 the following relationship shouldbe satisfied
1003816100381610038161003816119891119904minus1 minus 119891119904
1003816100381610038161003816 gt 0 (12)
4 Journal of Applied Mathematics
If 119904lowast = 119904 + 1 the objective function satisfies the followingequation
1 + 119863119904+1
(119904 + 1)ℎ=1 + 119863
119904+ sum119895119889119895119896
119904ℎ ( 119904119904 + 1)
ℎ
= (119891119904+sum119895119889119895119896
119904ℎ )(1 minus 1119904 + 1)
ℎ
(13)
From the definition of 119904 the following relationship shouldalso be satisfied
1003816100381610038161003816119891119904+1 minus 119891119904
1003816100381610038161003816 gt 0 (14)
From (11) and (12) we obtain the following relationship
119891119904gt (1 + (1119904 minus 1))ℎ (sum
119895119889119895119896119904)
(1 + (1119904 minus 1))ℎ minus 1 (15)
From (10) 119891119904le 1 Then (15) is transformed into the
following relationship
119904ℎ minus (119904 minus 1)ℎ gt sum119895
119889119895119896 (16)
The same process can be applied to (13) and (14) From(13) (14) and (16) we obtain the following
119878lowast
sum119895=1
119889119895119896+ (119904 minus 1)ℎ lt sℎ lt
119878lowast+1
sum119895=1
119889119895119896+ (119904 + 1)ℎ (17)
Equation (17) should be satisfied for all 119896 values There-foresum119878lowast
119895=1119889119895119896andsum119878lowast+1
119895=1119889119895119896can be respectively converted to
the maximum summation of 119889119895119896 which has a value of one
in the BSV (119889maxone) and the minimum summation of 119889119895119896
which has a value of zero in the BSV (119889minzero) Equation (17)is rewritten as follows
119889maxone + (119904 minus 1)ℎ lt sℎ lt 119889minzero + (119904 + 1)ℎ (18)
In this equation 119889maxone and 119889minzero are constant and 119904is determined by the HS process Therefore the value of ℎshould satisfy (18)
4 Experiment
In the experiments we used a commercial product theNeuroscan EEG signal acquisition device (Neuroscan systemCompumedics USA) to acquire the EEG signalsThis deviceconsists of the Synamps2 and Stim2 hardware and thesoftware for acquisition and preprocessing is Curry 7 Weused an EEG cap with 64 electrodes for the EEG signals andfive additional electrodes for noise reduction The subjectswere three healthy men in their mid-twenties The subjectswere selected without using any selection criteria to ensuregenerality of the results All subjects had undergone oneor more brain-wave experiments not related to this studyThe subjects were fully aware of the entire experimental
FP1 FP2FPz
Fz F2 F4 F6 F8F1F3F5F7
FCz FC2 FC4 FC6 FT8FC1FC3FC5FT7
Cz C2 C4 C6 T8C1C3C5T7
CPz CP2 CP4 CP6 TP8CP1CP3CP5TP7
Pz P2 P4 P6 P8P1P3P5P7
POz PO4 PO6 PO8
M2
PO3PO5PO7
M1
Oz O2O1
CB2CB1
AF3 AF4
Figure 1 Electrode placement in accordance with 64CH interna-tional 10ndash20 system
12 13 15 16 18 19 200 1 3 4 6 7 9 10
S1
S2
S3
(s)
Figure 2 Timing chart of the stimuli in one session
process risk and objective of the experiment The entireexperimental process was conducted under the experimentalethics and experimental safety regulations of the Chung-Ang University The subjects wore EEG caps on their headsand sat in a comfortable position in front of the monitorElectrodes were placed in accordance with the international10ndash20 system [26] The sampling rate of the EEG signals was250Hz and we used a band-pass filter to obtain signals from8 to 70Hz Commonly a low-frequency signal below 8Hzcontains biological noise such as blinking eyes and heartbeatThe following Figure 1 shows the location of the electrodesThe actual electrodes were placed on the human scalp so thatthe real position of the electrodes represented an arc In thisstudy however we installed the electrodes in a line for easein understanding the position of the electrodes
The subjects received three types of stimuli without feed-back To reduce noise the subjectmovements were restrictedThe experiment was repeated 10 times per subject Oneexperiment involved six sessions and one session consistedof seven epochs The order of the epochs was randomThe same epoch was repeated 60 times and each stimuluswas repeatedly administered 240 times The nonstationary
Journal of Applied Mathematics 5
minus5 0 5minus5
0
5
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5
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5
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5
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5
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5
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5
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5
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5
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5
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0
5
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5
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5
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0
5
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5
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5
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5
minus5 0 5minus5
0
5
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5
minus5 0 5minus5
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5
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5
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5
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5
minus5 0 5minus5
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5
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5
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0
5
minus5 0 5minus5
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5
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5
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5
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5
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5
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minus5 0 5minus5
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5
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5
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5
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5
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0
5
minus5 0 5minus5
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5
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5
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5
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5
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5
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minus5 0 5minus5
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5
Subject 1
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
Figure 3 Results of grouping for Subject 1
characteristic of the EEG signals allowed us to obtain resultsfrom a number of trials despite the small number of subjects
We used three types of stimuli in this study audiovisual and finger movement Each stimulus was given in1 s A monitor an earphone and a button pad were usedto introduce the stimuli which were given in rotation Thetiming of the stimuli in one session is presented in thefollowing timing chart Figure 2
S1 S2 and S3 represent the three stimuli and their orderwas randomly changedThe resting time between stimuli was2 s and the entire session lasted for 20 s First one stimuluswas administered three times Subsequently two stimuli wereconcurrently administered At the final three stimuli weregiven at once At 300ms before the motion stimulus wasgiven a white cross mark appeared on the black screen
The audio stimulus was a 1000-Hz stereo pure tone madeby Stim2 The monitor screen was black when the audiostimulus was administered with the earphone controlled bya specific amplifier for the least delay stimulus The visualstimulus was administered in 1 s when the monitor screenwas green and the other stimuli were controlled so that theunrelated channels were unaffectedThemotion stimulus wasquite different from the other stimuli because the subjectmade a physical movement by pressing the button pad usingtheir fingers The motion image was difficult to notice in theexperiments and affected the concentration of the subjectTherefore we used real movements in this stimulus eventhough noise from the movements was present To reducethe noise the subject used only one finger and kept the otherbody parts still The motion stimulus indicates a white cross
6 Journal of Applied Mathematics
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minus5 0 5minus5
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minus5 0 5minus5
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minus5 0 5minus5
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5
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minus5 0 5minus5
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minus5 0 5minus5
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minus5 0 5minus5
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minus5 0 5minus5
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5
minus5 0 5minus5
0
5
minus5 0 5minus5
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5
minus5 0 5minus5
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5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
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0
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0
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0
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Subject 2
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
s
Figure 4 Results of grouping for Subject 2
symbol on the screen to prepare the subject and a whitesentence ldquopress buttonrdquo appeared on the screen to signal tothe subject to push the button pad
41 Channel Grouping For the channel grouping we usedthe PSF HS algorithm and the MSE method for the objectivefunction In the previous research we used the binary HSand the correlated coefficient However the previous methodexhibited poor performance in terms of computational timeIn this study we used the proposed method to improve thespeed and accuracy The number of iterations of the HS andHMS used in this study was 5000 and 20 respectively Thenumber of trials for one epoch of the HS algorithm was 10We identified the groups by determining the number of timeseach channel was grouped with the other channels All 30experiments for the three subjects were summarized in threeresults in Section 51
42 Feature Classification In the classification we used theHS algorithm with PSF where the HMS was 20 and thenumber of iterations was 50000 The results of channelgrouping which dimension of the signal data decreased wereused for classification The CSP algorithm was used forfeature extraction To match the number of channels we
selected a minimum number of channels among the stimuliIn the CSP procedure we used the data from the given twostimuli and determined the eigenvectors of the filtered signalThe data were classified without using any training method
5 Results and Analysis
51 Channel Grouping The results of the channel groupingare shown in the following Figures 3 4 and 5 and Table 1We searched the group by comparing all cases The resultsfor subject 1 exhibited were distinct The audio stimulusactivated the front of the brain the visual stimulus activatedthe backside of the brain and the motion stimulus activatedthe middle of the brain Generally the functions of the brainare known to be specialized by the locationThe area startingwith ldquoCrdquo includes the vicinity from the Cz channel to theC6 channel which is also known as the motor cortex Thevisual stimulus activated the visual cortex and the back ofthe brain A similar tendency could be observed in the othersubjects All subjects showed the relationship between themotion stimulus and the ldquoCrdquo area Although slight differencesexisted the results agreed with those of the existing researchwhich also explained the visual and audio stimuli Therefore
Journal of Applied Mathematics 7
minus5 0 5minus5
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5
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5
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5
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5
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5
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5
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5
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5
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5
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5
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0
5
minus5 0 5minus5
0
5
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5
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5
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5
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5
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5
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5
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5
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5
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5
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5
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5
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5
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5
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5
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0
5
Subject 3
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
Figure 5 Results of grouping for Subject 3
Table 1 Channel group results
Subject Stimuli Channel name
Subject 1Audio FP1 AF3 FT7 FC5 FC3 FC1 FCzVisual P5 P3 P1 Pz P2 P4 PO4Motions C5 C3 C1 Cz C2
Subject 2Audio P5 P3 P1 PzVisual PO7 PO5 PO3 POz O1Motions C5 C3 C1 Cz C2 CP2
Subject 3Audio F7 F5 F3 F1 Fz F2 F4 AF4Visual PO3 POz PO4 PO6 PO8 O1 O2 CB1Motions C5 C3 C1 Cz
we conclude that the results of the grouping are related to thegiven stimuli
Furthermore the similar groupmade by a single stimulusalso could be found in the groups subjected to two or threestimuli including a previous single stimulus Therefore wecan analyze the EEG and the brain by separated stimuliAdditionally the number of channels used in the analysis wasreduced by the proposed method These characteristics canmake the EEG analysis easier
However in some cases determining the relationshipbetween the stimulus and the group was difficult Thisproblem implied two limitations of this work First is thedifficulty in strictly controlling the experimental conditionsincluding the state of the subject A human brain constantlyand unconsciously receives stimuli If an unwanted stimulusis administered the accuracy would worsen The second isthe problem resulting from averaging the signals EEG signalshave a high temporal resolution and the data sampling rateused in this studywas 250HzHowever the groupingmethod
8 Journal of Applied Mathematics
Audio-visual
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 6 Classification accuracy of the audio and visual stimuli
Visual-motion
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 7 Classification accuracy of the visual and motion stimuli
focused on the summation of the MSEs in 1 s Therefore theadvantage of EEG was not well presented
52 Feature Classification We used the HS algorithm forclassification The classification in the feature space wasattempted 10 times for 120 features Figures 6 7 and 8 showthe results of the classification for two stimuli
For Subject 1 the best classification result was 7417and the worst classification result was 55 The averageaccuracy rate was 6283 For Subject 2 the best classificationresult was 7750 and the worst classification result was55 The average accuracy rate was 6461 For Subject3 the best classification result was 7250 and the worstclassification result was 5083 The average accuracy ratewas 6253 The HS algorithm is a heuristic algorithm thusthe results had an accuracy-rate range However without anylearning method or prior information the proposed method
Audio-motion
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 8 Classification accuracy of the audio and motion stimuli
exceeded the decision boundary of the two classes by a rateof approximately 63 Furthermore the classified data werenonlinear high-dimensional data
6 Conclusion
In this study we have built a group to easily analyze EEG sig-nals and proposed an EEG signal classificationmethod basedon HS Signal processing and classification are important asthey determine the accuracy of the entire system Thereforewe focused on solving the problems of a BCI system insignal processing and classification First we employed EEGsignal grouping using theHS algorithm to reduce the channelcomplexity From the EEG signal grouping we did not onlyreduce the channel used in the analysis but also separatelyanalyzed the signal of each stimulus Therefore the compu-tational time and the complexity of the data were reducedThe proposedmethod could be the smartest solution for EEGsignal analysis for problems with large data and dimensionNext we classified the EEG signals using HS We proposed anovel classification method with a metaheuristic algorithmfor nonlinear data such as the EEG signals The proposedmethod also focused on unsupervised classification thus wecould classify the data without training or prior informationWe believe that the proposed two methods could be helpfulin enhancing the BCI implementation and the EEG analysisfield
Acknowledgment
This work was supported by the Mid-Career ResearcherProgram through an NRF Grant funded by the MEST (no2012-0008726)
Journal of Applied Mathematics 9
References
[1] L Bi X Fan and Y Liu ldquoEEG-based brain-controlled mobilerobots a surveyrdquo IEEE Transactions on Human-Machine Sys-tems vol 43 no 2 pp 161ndash176 2013
[2] I Iturrate J M Antelis A Kubler and J Minguez ldquoA nonin-vasive brain-actuated wheelchair based on a P300 neurophysi-ological protocol and automated navigationrdquo IEEE Transactionson Robotics vol 25 no 3 pp 614ndash627 2009
[3] A Frisoli C Loconsole D Leonardis et al ldquoA new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitationin real-world tasksrdquo IEEE Transactions on Systems Man andCybernetics C vol 42 no 6 pp 1169ndash1179 2012
[4] J R Wolpaw ldquoBrain-computer interface research comes of agetraditional assumptions meet emerging realitiesrdquo Journal ofMotor Behavior vol 42 no 6 pp 351ndash353 2010
[5] E E Fetz ldquoReal-time control of a robotic arm by neuronalensemblesrdquoNature Neuroscience vol 2 no 7 pp 583ndash584 1999
[6] P LeVan J Maclaren M Herbst R Sostheim M Zaitsev andJ Hennig ldquoBallistocardiographic artifact removal from simul-taneous EEG-fMRI using an optical motion-tracking systemrdquoNeuroImage vol 75 pp 1ndash11 2013
[7] A Ishikawa H Udagawa YMasuda S Kohno T Amita and YInoue ldquoDevelopment of double density whole brain fNIRSwithEEG system for brain machine interfacerdquo in Proceedings of the33rd Annual International Conference of the IEEE Engineeringin Medicine and Biology Society (EMBS rsquo11) pp 6118ndash6122September 2011
[8] O A P Sosa Y Quijano M Doniz and J E Chong-QueroldquoBCI a historical analysis and technology comparisonrdquo in Pro-ceedings of the Pan American Health Care Exchanges Conference(PAHCE rsquo11) pp 205ndash209 Rio de Janeiro Brazil April 2011
[9] Y O Halchenko S J Hanson and B A Pearlmutter ldquoMulti-modal integration FMRIMRI EEGMEGrdquo inAdvanced ImageProcessing in Magnetic Resonance Imaging CRC Press BocaRaton Fla USA 2005
[10] T N Lal M Schroder T Hinterberger et al ldquoSupport vectorchannel selection in BCIrdquo IEEE Transactions on BiomedicalEngineering vol 51 no 6 pp 1003ndash1010 2004
[11] J Meng G Huang D Zhang and X Zhu ldquoOptimizing spatialspectral patterns jointly with channel configuration for brain-computer interfacerdquoNeurocomputing vol 104 pp 115ndash126 2013
[12] C Brunner M Naeem and G Pfurtscheller ldquoDimensionalityreduction and channel selection of motor imagery electroen-cephalographic datardquo Computational Intelligence and Neuro-science vol 2009 Article ID 537504 8 pages 2009
[13] H Lu H-L Eng C Guan K N Plataniotis and A NVenetsanopoulos ldquoRegularized common spatial pattern withaggregation for EEG Classification in small-sample settingrdquoIEEE Transactions on Biomedical Engineering vol 57 no 12 pp2936ndash2946 2010
[14] B Wang C M Wong F Wan P U Mak P-I Mak and M IVai ldquoTrial pruning based on genetic algorithm for single-trialEEG classificationrdquo Computers and Electrical Engineering vol38 no 1 pp 35ndash44 2012
[15] B Blankertz S Lemm M Treder S Haufe and K-R MullerldquoSingle-trial analysis and classification of ERP components atutorialrdquo NeuroImage vol 56 no 2 pp 814ndash825 2011
[16] S Siuly and Y Li ldquoImproving the separability of motor imageryEEG signals using a cross correlation-based least square support
vector machine for brain-computer interfacerdquo IEEE Transac-tions on Neural Systems and Rehabilitation Engineering vol 20no 4 pp 526ndash538 2012
[17] A Vuckovic and F Sepulveda ldquoDelta band contribution incue based single trial classification of real and imaginary wristmovementsrdquo Medical and Biological Engineering and Comput-ing vol 46 no 6 pp 529ndash539 2008
[18] K S Lee and Z W Geem ldquoA new meta-heuristic algorithm forcontinuous engineering optimization harmony search theoryand practicerdquo Computer Methods in Applied Mechanics andEngineering vol 194 no 36ndash38 pp 3902ndash3933 2005
[19] X-S Yang ldquoHarmony search as a metaheuristic algorithmrdquoMusic-Inspired Harmony Search Algorithm vol 191 pp 1ndash142009
[20] Z W Geem J H Kim and G V Loganathan ldquoA new heuristicoptimization algorithm harmony searchrdquo Simulation vol 76no 2 pp 60ndash68 2001
[21] Q-K Pan P N Suganthan M F Tasgetiren and J J LiangldquoA self-adaptive global best harmony search algorithm forcontinuous optimization problemsrdquo Applied Mathematics andComputation vol 216 no 3 pp 830ndash848 2010
[22] L D S Coelho and V C Mariani ldquoAn improved harmonysearch algorithm for power economic load dispatchrdquo EnergyConversion and Management vol 50 no 10 pp 2522ndash25262009
[23] Z W Geem and K-B Sim ldquoParameter-setting-free harmonysearch algorithmrdquo Applied Mathematics and Computation vol217 no 8 pp 3881ndash3889 2010
[24] M G Wentrup and M Buss ldquoMulticlass common spatialpatterns and information theoretic feature extractionrdquo IEEETransactions on Biomedical Engineering vol 55 no 8 pp 1991ndash2000 2008
[25] L Wang Y Mao Q Niu and M Fei ldquoA multi-objective binaryharmony search algorithmrdquoAdvances in Swarm Intelligence vol6729 no 2 pp 74ndash81 2011
[26] E Niedermeyer and F L da Silva ldquoEEG recording andoperation of the apparatusrdquo in Electroencephalography BasicPrinciples Clinical Applications and Related Fields LippincottWilliams and Wilkins Baltimore Md USA 2004
Submit your manuscripts athttpwwwhindawicom
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Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Discrete Dynamics in Nature and Society
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Decision SciencesAdvances in
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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
4 Journal of Applied Mathematics
If 119904lowast = 119904 + 1 the objective function satisfies the followingequation
1 + 119863119904+1
(119904 + 1)ℎ=1 + 119863
119904+ sum119895119889119895119896
119904ℎ ( 119904119904 + 1)
ℎ
= (119891119904+sum119895119889119895119896
119904ℎ )(1 minus 1119904 + 1)
ℎ
(13)
From the definition of 119904 the following relationship shouldalso be satisfied
1003816100381610038161003816119891119904+1 minus 119891119904
1003816100381610038161003816 gt 0 (14)
From (11) and (12) we obtain the following relationship
119891119904gt (1 + (1119904 minus 1))ℎ (sum
119895119889119895119896119904)
(1 + (1119904 minus 1))ℎ minus 1 (15)
From (10) 119891119904le 1 Then (15) is transformed into the
following relationship
119904ℎ minus (119904 minus 1)ℎ gt sum119895
119889119895119896 (16)
The same process can be applied to (13) and (14) From(13) (14) and (16) we obtain the following
119878lowast
sum119895=1
119889119895119896+ (119904 minus 1)ℎ lt sℎ lt
119878lowast+1
sum119895=1
119889119895119896+ (119904 + 1)ℎ (17)
Equation (17) should be satisfied for all 119896 values There-foresum119878lowast
119895=1119889119895119896andsum119878lowast+1
119895=1119889119895119896can be respectively converted to
the maximum summation of 119889119895119896 which has a value of one
in the BSV (119889maxone) and the minimum summation of 119889119895119896
which has a value of zero in the BSV (119889minzero) Equation (17)is rewritten as follows
119889maxone + (119904 minus 1)ℎ lt sℎ lt 119889minzero + (119904 + 1)ℎ (18)
In this equation 119889maxone and 119889minzero are constant and 119904is determined by the HS process Therefore the value of ℎshould satisfy (18)
4 Experiment
In the experiments we used a commercial product theNeuroscan EEG signal acquisition device (Neuroscan systemCompumedics USA) to acquire the EEG signalsThis deviceconsists of the Synamps2 and Stim2 hardware and thesoftware for acquisition and preprocessing is Curry 7 Weused an EEG cap with 64 electrodes for the EEG signals andfive additional electrodes for noise reduction The subjectswere three healthy men in their mid-twenties The subjectswere selected without using any selection criteria to ensuregenerality of the results All subjects had undergone oneor more brain-wave experiments not related to this studyThe subjects were fully aware of the entire experimental
FP1 FP2FPz
Fz F2 F4 F6 F8F1F3F5F7
FCz FC2 FC4 FC6 FT8FC1FC3FC5FT7
Cz C2 C4 C6 T8C1C3C5T7
CPz CP2 CP4 CP6 TP8CP1CP3CP5TP7
Pz P2 P4 P6 P8P1P3P5P7
POz PO4 PO6 PO8
M2
PO3PO5PO7
M1
Oz O2O1
CB2CB1
AF3 AF4
Figure 1 Electrode placement in accordance with 64CH interna-tional 10ndash20 system
12 13 15 16 18 19 200 1 3 4 6 7 9 10
S1
S2
S3
(s)
Figure 2 Timing chart of the stimuli in one session
process risk and objective of the experiment The entireexperimental process was conducted under the experimentalethics and experimental safety regulations of the Chung-Ang University The subjects wore EEG caps on their headsand sat in a comfortable position in front of the monitorElectrodes were placed in accordance with the international10ndash20 system [26] The sampling rate of the EEG signals was250Hz and we used a band-pass filter to obtain signals from8 to 70Hz Commonly a low-frequency signal below 8Hzcontains biological noise such as blinking eyes and heartbeatThe following Figure 1 shows the location of the electrodesThe actual electrodes were placed on the human scalp so thatthe real position of the electrodes represented an arc In thisstudy however we installed the electrodes in a line for easein understanding the position of the electrodes
The subjects received three types of stimuli without feed-back To reduce noise the subjectmovements were restrictedThe experiment was repeated 10 times per subject Oneexperiment involved six sessions and one session consistedof seven epochs The order of the epochs was randomThe same epoch was repeated 60 times and each stimuluswas repeatedly administered 240 times The nonstationary
Journal of Applied Mathematics 5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
Subject 1
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
Figure 3 Results of grouping for Subject 1
characteristic of the EEG signals allowed us to obtain resultsfrom a number of trials despite the small number of subjects
We used three types of stimuli in this study audiovisual and finger movement Each stimulus was given in1 s A monitor an earphone and a button pad were usedto introduce the stimuli which were given in rotation Thetiming of the stimuli in one session is presented in thefollowing timing chart Figure 2
S1 S2 and S3 represent the three stimuli and their orderwas randomly changedThe resting time between stimuli was2 s and the entire session lasted for 20 s First one stimuluswas administered three times Subsequently two stimuli wereconcurrently administered At the final three stimuli weregiven at once At 300ms before the motion stimulus wasgiven a white cross mark appeared on the black screen
The audio stimulus was a 1000-Hz stereo pure tone madeby Stim2 The monitor screen was black when the audiostimulus was administered with the earphone controlled bya specific amplifier for the least delay stimulus The visualstimulus was administered in 1 s when the monitor screenwas green and the other stimuli were controlled so that theunrelated channels were unaffectedThemotion stimulus wasquite different from the other stimuli because the subjectmade a physical movement by pressing the button pad usingtheir fingers The motion image was difficult to notice in theexperiments and affected the concentration of the subjectTherefore we used real movements in this stimulus eventhough noise from the movements was present To reducethe noise the subject used only one finger and kept the otherbody parts still The motion stimulus indicates a white cross
6 Journal of Applied Mathematics
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
Subject 2
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
s
Figure 4 Results of grouping for Subject 2
symbol on the screen to prepare the subject and a whitesentence ldquopress buttonrdquo appeared on the screen to signal tothe subject to push the button pad
41 Channel Grouping For the channel grouping we usedthe PSF HS algorithm and the MSE method for the objectivefunction In the previous research we used the binary HSand the correlated coefficient However the previous methodexhibited poor performance in terms of computational timeIn this study we used the proposed method to improve thespeed and accuracy The number of iterations of the HS andHMS used in this study was 5000 and 20 respectively Thenumber of trials for one epoch of the HS algorithm was 10We identified the groups by determining the number of timeseach channel was grouped with the other channels All 30experiments for the three subjects were summarized in threeresults in Section 51
42 Feature Classification In the classification we used theHS algorithm with PSF where the HMS was 20 and thenumber of iterations was 50000 The results of channelgrouping which dimension of the signal data decreased wereused for classification The CSP algorithm was used forfeature extraction To match the number of channels we
selected a minimum number of channels among the stimuliIn the CSP procedure we used the data from the given twostimuli and determined the eigenvectors of the filtered signalThe data were classified without using any training method
5 Results and Analysis
51 Channel Grouping The results of the channel groupingare shown in the following Figures 3 4 and 5 and Table 1We searched the group by comparing all cases The resultsfor subject 1 exhibited were distinct The audio stimulusactivated the front of the brain the visual stimulus activatedthe backside of the brain and the motion stimulus activatedthe middle of the brain Generally the functions of the brainare known to be specialized by the locationThe area startingwith ldquoCrdquo includes the vicinity from the Cz channel to theC6 channel which is also known as the motor cortex Thevisual stimulus activated the visual cortex and the back ofthe brain A similar tendency could be observed in the othersubjects All subjects showed the relationship between themotion stimulus and the ldquoCrdquo area Although slight differencesexisted the results agreed with those of the existing researchwhich also explained the visual and audio stimuli Therefore
Journal of Applied Mathematics 7
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
Subject 3
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
Figure 5 Results of grouping for Subject 3
Table 1 Channel group results
Subject Stimuli Channel name
Subject 1Audio FP1 AF3 FT7 FC5 FC3 FC1 FCzVisual P5 P3 P1 Pz P2 P4 PO4Motions C5 C3 C1 Cz C2
Subject 2Audio P5 P3 P1 PzVisual PO7 PO5 PO3 POz O1Motions C5 C3 C1 Cz C2 CP2
Subject 3Audio F7 F5 F3 F1 Fz F2 F4 AF4Visual PO3 POz PO4 PO6 PO8 O1 O2 CB1Motions C5 C3 C1 Cz
we conclude that the results of the grouping are related to thegiven stimuli
Furthermore the similar groupmade by a single stimulusalso could be found in the groups subjected to two or threestimuli including a previous single stimulus Therefore wecan analyze the EEG and the brain by separated stimuliAdditionally the number of channels used in the analysis wasreduced by the proposed method These characteristics canmake the EEG analysis easier
However in some cases determining the relationshipbetween the stimulus and the group was difficult Thisproblem implied two limitations of this work First is thedifficulty in strictly controlling the experimental conditionsincluding the state of the subject A human brain constantlyand unconsciously receives stimuli If an unwanted stimulusis administered the accuracy would worsen The second isthe problem resulting from averaging the signals EEG signalshave a high temporal resolution and the data sampling rateused in this studywas 250HzHowever the groupingmethod
8 Journal of Applied Mathematics
Audio-visual
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 6 Classification accuracy of the audio and visual stimuli
Visual-motion
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 7 Classification accuracy of the visual and motion stimuli
focused on the summation of the MSEs in 1 s Therefore theadvantage of EEG was not well presented
52 Feature Classification We used the HS algorithm forclassification The classification in the feature space wasattempted 10 times for 120 features Figures 6 7 and 8 showthe results of the classification for two stimuli
For Subject 1 the best classification result was 7417and the worst classification result was 55 The averageaccuracy rate was 6283 For Subject 2 the best classificationresult was 7750 and the worst classification result was55 The average accuracy rate was 6461 For Subject3 the best classification result was 7250 and the worstclassification result was 5083 The average accuracy ratewas 6253 The HS algorithm is a heuristic algorithm thusthe results had an accuracy-rate range However without anylearning method or prior information the proposed method
Audio-motion
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 8 Classification accuracy of the audio and motion stimuli
exceeded the decision boundary of the two classes by a rateof approximately 63 Furthermore the classified data werenonlinear high-dimensional data
6 Conclusion
In this study we have built a group to easily analyze EEG sig-nals and proposed an EEG signal classificationmethod basedon HS Signal processing and classification are important asthey determine the accuracy of the entire system Thereforewe focused on solving the problems of a BCI system insignal processing and classification First we employed EEGsignal grouping using theHS algorithm to reduce the channelcomplexity From the EEG signal grouping we did not onlyreduce the channel used in the analysis but also separatelyanalyzed the signal of each stimulus Therefore the compu-tational time and the complexity of the data were reducedThe proposedmethod could be the smartest solution for EEGsignal analysis for problems with large data and dimensionNext we classified the EEG signals using HS We proposed anovel classification method with a metaheuristic algorithmfor nonlinear data such as the EEG signals The proposedmethod also focused on unsupervised classification thus wecould classify the data without training or prior informationWe believe that the proposed two methods could be helpfulin enhancing the BCI implementation and the EEG analysisfield
Acknowledgment
This work was supported by the Mid-Career ResearcherProgram through an NRF Grant funded by the MEST (no2012-0008726)
Journal of Applied Mathematics 9
References
[1] L Bi X Fan and Y Liu ldquoEEG-based brain-controlled mobilerobots a surveyrdquo IEEE Transactions on Human-Machine Sys-tems vol 43 no 2 pp 161ndash176 2013
[2] I Iturrate J M Antelis A Kubler and J Minguez ldquoA nonin-vasive brain-actuated wheelchair based on a P300 neurophysi-ological protocol and automated navigationrdquo IEEE Transactionson Robotics vol 25 no 3 pp 614ndash627 2009
[3] A Frisoli C Loconsole D Leonardis et al ldquoA new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitationin real-world tasksrdquo IEEE Transactions on Systems Man andCybernetics C vol 42 no 6 pp 1169ndash1179 2012
[4] J R Wolpaw ldquoBrain-computer interface research comes of agetraditional assumptions meet emerging realitiesrdquo Journal ofMotor Behavior vol 42 no 6 pp 351ndash353 2010
[5] E E Fetz ldquoReal-time control of a robotic arm by neuronalensemblesrdquoNature Neuroscience vol 2 no 7 pp 583ndash584 1999
[6] P LeVan J Maclaren M Herbst R Sostheim M Zaitsev andJ Hennig ldquoBallistocardiographic artifact removal from simul-taneous EEG-fMRI using an optical motion-tracking systemrdquoNeuroImage vol 75 pp 1ndash11 2013
[7] A Ishikawa H Udagawa YMasuda S Kohno T Amita and YInoue ldquoDevelopment of double density whole brain fNIRSwithEEG system for brain machine interfacerdquo in Proceedings of the33rd Annual International Conference of the IEEE Engineeringin Medicine and Biology Society (EMBS rsquo11) pp 6118ndash6122September 2011
[8] O A P Sosa Y Quijano M Doniz and J E Chong-QueroldquoBCI a historical analysis and technology comparisonrdquo in Pro-ceedings of the Pan American Health Care Exchanges Conference(PAHCE rsquo11) pp 205ndash209 Rio de Janeiro Brazil April 2011
[9] Y O Halchenko S J Hanson and B A Pearlmutter ldquoMulti-modal integration FMRIMRI EEGMEGrdquo inAdvanced ImageProcessing in Magnetic Resonance Imaging CRC Press BocaRaton Fla USA 2005
[10] T N Lal M Schroder T Hinterberger et al ldquoSupport vectorchannel selection in BCIrdquo IEEE Transactions on BiomedicalEngineering vol 51 no 6 pp 1003ndash1010 2004
[11] J Meng G Huang D Zhang and X Zhu ldquoOptimizing spatialspectral patterns jointly with channel configuration for brain-computer interfacerdquoNeurocomputing vol 104 pp 115ndash126 2013
[12] C Brunner M Naeem and G Pfurtscheller ldquoDimensionalityreduction and channel selection of motor imagery electroen-cephalographic datardquo Computational Intelligence and Neuro-science vol 2009 Article ID 537504 8 pages 2009
[13] H Lu H-L Eng C Guan K N Plataniotis and A NVenetsanopoulos ldquoRegularized common spatial pattern withaggregation for EEG Classification in small-sample settingrdquoIEEE Transactions on Biomedical Engineering vol 57 no 12 pp2936ndash2946 2010
[14] B Wang C M Wong F Wan P U Mak P-I Mak and M IVai ldquoTrial pruning based on genetic algorithm for single-trialEEG classificationrdquo Computers and Electrical Engineering vol38 no 1 pp 35ndash44 2012
[15] B Blankertz S Lemm M Treder S Haufe and K-R MullerldquoSingle-trial analysis and classification of ERP components atutorialrdquo NeuroImage vol 56 no 2 pp 814ndash825 2011
[16] S Siuly and Y Li ldquoImproving the separability of motor imageryEEG signals using a cross correlation-based least square support
vector machine for brain-computer interfacerdquo IEEE Transac-tions on Neural Systems and Rehabilitation Engineering vol 20no 4 pp 526ndash538 2012
[17] A Vuckovic and F Sepulveda ldquoDelta band contribution incue based single trial classification of real and imaginary wristmovementsrdquo Medical and Biological Engineering and Comput-ing vol 46 no 6 pp 529ndash539 2008
[18] K S Lee and Z W Geem ldquoA new meta-heuristic algorithm forcontinuous engineering optimization harmony search theoryand practicerdquo Computer Methods in Applied Mechanics andEngineering vol 194 no 36ndash38 pp 3902ndash3933 2005
[19] X-S Yang ldquoHarmony search as a metaheuristic algorithmrdquoMusic-Inspired Harmony Search Algorithm vol 191 pp 1ndash142009
[20] Z W Geem J H Kim and G V Loganathan ldquoA new heuristicoptimization algorithm harmony searchrdquo Simulation vol 76no 2 pp 60ndash68 2001
[21] Q-K Pan P N Suganthan M F Tasgetiren and J J LiangldquoA self-adaptive global best harmony search algorithm forcontinuous optimization problemsrdquo Applied Mathematics andComputation vol 216 no 3 pp 830ndash848 2010
[22] L D S Coelho and V C Mariani ldquoAn improved harmonysearch algorithm for power economic load dispatchrdquo EnergyConversion and Management vol 50 no 10 pp 2522ndash25262009
[23] Z W Geem and K-B Sim ldquoParameter-setting-free harmonysearch algorithmrdquo Applied Mathematics and Computation vol217 no 8 pp 3881ndash3889 2010
[24] M G Wentrup and M Buss ldquoMulticlass common spatialpatterns and information theoretic feature extractionrdquo IEEETransactions on Biomedical Engineering vol 55 no 8 pp 1991ndash2000 2008
[25] L Wang Y Mao Q Niu and M Fei ldquoA multi-objective binaryharmony search algorithmrdquoAdvances in Swarm Intelligence vol6729 no 2 pp 74ndash81 2011
[26] E Niedermeyer and F L da Silva ldquoEEG recording andoperation of the apparatusrdquo in Electroencephalography BasicPrinciples Clinical Applications and Related Fields LippincottWilliams and Wilkins Baltimore Md USA 2004
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Differential EquationsInternational Journal of
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OptimizationJournal of
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International Journal of
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Operations ResearchAdvances in
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Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
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Discrete Dynamics in Nature and Society
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Decision SciencesAdvances in
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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Journal of Applied Mathematics 5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
Subject 1
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
Figure 3 Results of grouping for Subject 1
characteristic of the EEG signals allowed us to obtain resultsfrom a number of trials despite the small number of subjects
We used three types of stimuli in this study audiovisual and finger movement Each stimulus was given in1 s A monitor an earphone and a button pad were usedto introduce the stimuli which were given in rotation Thetiming of the stimuli in one session is presented in thefollowing timing chart Figure 2
S1 S2 and S3 represent the three stimuli and their orderwas randomly changedThe resting time between stimuli was2 s and the entire session lasted for 20 s First one stimuluswas administered three times Subsequently two stimuli wereconcurrently administered At the final three stimuli weregiven at once At 300ms before the motion stimulus wasgiven a white cross mark appeared on the black screen
The audio stimulus was a 1000-Hz stereo pure tone madeby Stim2 The monitor screen was black when the audiostimulus was administered with the earphone controlled bya specific amplifier for the least delay stimulus The visualstimulus was administered in 1 s when the monitor screenwas green and the other stimuli were controlled so that theunrelated channels were unaffectedThemotion stimulus wasquite different from the other stimuli because the subjectmade a physical movement by pressing the button pad usingtheir fingers The motion image was difficult to notice in theexperiments and affected the concentration of the subjectTherefore we used real movements in this stimulus eventhough noise from the movements was present To reducethe noise the subject used only one finger and kept the otherbody parts still The motion stimulus indicates a white cross
6 Journal of Applied Mathematics
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
Subject 2
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
s
Figure 4 Results of grouping for Subject 2
symbol on the screen to prepare the subject and a whitesentence ldquopress buttonrdquo appeared on the screen to signal tothe subject to push the button pad
41 Channel Grouping For the channel grouping we usedthe PSF HS algorithm and the MSE method for the objectivefunction In the previous research we used the binary HSand the correlated coefficient However the previous methodexhibited poor performance in terms of computational timeIn this study we used the proposed method to improve thespeed and accuracy The number of iterations of the HS andHMS used in this study was 5000 and 20 respectively Thenumber of trials for one epoch of the HS algorithm was 10We identified the groups by determining the number of timeseach channel was grouped with the other channels All 30experiments for the three subjects were summarized in threeresults in Section 51
42 Feature Classification In the classification we used theHS algorithm with PSF where the HMS was 20 and thenumber of iterations was 50000 The results of channelgrouping which dimension of the signal data decreased wereused for classification The CSP algorithm was used forfeature extraction To match the number of channels we
selected a minimum number of channels among the stimuliIn the CSP procedure we used the data from the given twostimuli and determined the eigenvectors of the filtered signalThe data were classified without using any training method
5 Results and Analysis
51 Channel Grouping The results of the channel groupingare shown in the following Figures 3 4 and 5 and Table 1We searched the group by comparing all cases The resultsfor subject 1 exhibited were distinct The audio stimulusactivated the front of the brain the visual stimulus activatedthe backside of the brain and the motion stimulus activatedthe middle of the brain Generally the functions of the brainare known to be specialized by the locationThe area startingwith ldquoCrdquo includes the vicinity from the Cz channel to theC6 channel which is also known as the motor cortex Thevisual stimulus activated the visual cortex and the back ofthe brain A similar tendency could be observed in the othersubjects All subjects showed the relationship between themotion stimulus and the ldquoCrdquo area Although slight differencesexisted the results agreed with those of the existing researchwhich also explained the visual and audio stimuli Therefore
Journal of Applied Mathematics 7
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
Subject 3
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
Figure 5 Results of grouping for Subject 3
Table 1 Channel group results
Subject Stimuli Channel name
Subject 1Audio FP1 AF3 FT7 FC5 FC3 FC1 FCzVisual P5 P3 P1 Pz P2 P4 PO4Motions C5 C3 C1 Cz C2
Subject 2Audio P5 P3 P1 PzVisual PO7 PO5 PO3 POz O1Motions C5 C3 C1 Cz C2 CP2
Subject 3Audio F7 F5 F3 F1 Fz F2 F4 AF4Visual PO3 POz PO4 PO6 PO8 O1 O2 CB1Motions C5 C3 C1 Cz
we conclude that the results of the grouping are related to thegiven stimuli
Furthermore the similar groupmade by a single stimulusalso could be found in the groups subjected to two or threestimuli including a previous single stimulus Therefore wecan analyze the EEG and the brain by separated stimuliAdditionally the number of channels used in the analysis wasreduced by the proposed method These characteristics canmake the EEG analysis easier
However in some cases determining the relationshipbetween the stimulus and the group was difficult Thisproblem implied two limitations of this work First is thedifficulty in strictly controlling the experimental conditionsincluding the state of the subject A human brain constantlyand unconsciously receives stimuli If an unwanted stimulusis administered the accuracy would worsen The second isthe problem resulting from averaging the signals EEG signalshave a high temporal resolution and the data sampling rateused in this studywas 250HzHowever the groupingmethod
8 Journal of Applied Mathematics
Audio-visual
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 6 Classification accuracy of the audio and visual stimuli
Visual-motion
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 7 Classification accuracy of the visual and motion stimuli
focused on the summation of the MSEs in 1 s Therefore theadvantage of EEG was not well presented
52 Feature Classification We used the HS algorithm forclassification The classification in the feature space wasattempted 10 times for 120 features Figures 6 7 and 8 showthe results of the classification for two stimuli
For Subject 1 the best classification result was 7417and the worst classification result was 55 The averageaccuracy rate was 6283 For Subject 2 the best classificationresult was 7750 and the worst classification result was55 The average accuracy rate was 6461 For Subject3 the best classification result was 7250 and the worstclassification result was 5083 The average accuracy ratewas 6253 The HS algorithm is a heuristic algorithm thusthe results had an accuracy-rate range However without anylearning method or prior information the proposed method
Audio-motion
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 8 Classification accuracy of the audio and motion stimuli
exceeded the decision boundary of the two classes by a rateof approximately 63 Furthermore the classified data werenonlinear high-dimensional data
6 Conclusion
In this study we have built a group to easily analyze EEG sig-nals and proposed an EEG signal classificationmethod basedon HS Signal processing and classification are important asthey determine the accuracy of the entire system Thereforewe focused on solving the problems of a BCI system insignal processing and classification First we employed EEGsignal grouping using theHS algorithm to reduce the channelcomplexity From the EEG signal grouping we did not onlyreduce the channel used in the analysis but also separatelyanalyzed the signal of each stimulus Therefore the compu-tational time and the complexity of the data were reducedThe proposedmethod could be the smartest solution for EEGsignal analysis for problems with large data and dimensionNext we classified the EEG signals using HS We proposed anovel classification method with a metaheuristic algorithmfor nonlinear data such as the EEG signals The proposedmethod also focused on unsupervised classification thus wecould classify the data without training or prior informationWe believe that the proposed two methods could be helpfulin enhancing the BCI implementation and the EEG analysisfield
Acknowledgment
This work was supported by the Mid-Career ResearcherProgram through an NRF Grant funded by the MEST (no2012-0008726)
Journal of Applied Mathematics 9
References
[1] L Bi X Fan and Y Liu ldquoEEG-based brain-controlled mobilerobots a surveyrdquo IEEE Transactions on Human-Machine Sys-tems vol 43 no 2 pp 161ndash176 2013
[2] I Iturrate J M Antelis A Kubler and J Minguez ldquoA nonin-vasive brain-actuated wheelchair based on a P300 neurophysi-ological protocol and automated navigationrdquo IEEE Transactionson Robotics vol 25 no 3 pp 614ndash627 2009
[3] A Frisoli C Loconsole D Leonardis et al ldquoA new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitationin real-world tasksrdquo IEEE Transactions on Systems Man andCybernetics C vol 42 no 6 pp 1169ndash1179 2012
[4] J R Wolpaw ldquoBrain-computer interface research comes of agetraditional assumptions meet emerging realitiesrdquo Journal ofMotor Behavior vol 42 no 6 pp 351ndash353 2010
[5] E E Fetz ldquoReal-time control of a robotic arm by neuronalensemblesrdquoNature Neuroscience vol 2 no 7 pp 583ndash584 1999
[6] P LeVan J Maclaren M Herbst R Sostheim M Zaitsev andJ Hennig ldquoBallistocardiographic artifact removal from simul-taneous EEG-fMRI using an optical motion-tracking systemrdquoNeuroImage vol 75 pp 1ndash11 2013
[7] A Ishikawa H Udagawa YMasuda S Kohno T Amita and YInoue ldquoDevelopment of double density whole brain fNIRSwithEEG system for brain machine interfacerdquo in Proceedings of the33rd Annual International Conference of the IEEE Engineeringin Medicine and Biology Society (EMBS rsquo11) pp 6118ndash6122September 2011
[8] O A P Sosa Y Quijano M Doniz and J E Chong-QueroldquoBCI a historical analysis and technology comparisonrdquo in Pro-ceedings of the Pan American Health Care Exchanges Conference(PAHCE rsquo11) pp 205ndash209 Rio de Janeiro Brazil April 2011
[9] Y O Halchenko S J Hanson and B A Pearlmutter ldquoMulti-modal integration FMRIMRI EEGMEGrdquo inAdvanced ImageProcessing in Magnetic Resonance Imaging CRC Press BocaRaton Fla USA 2005
[10] T N Lal M Schroder T Hinterberger et al ldquoSupport vectorchannel selection in BCIrdquo IEEE Transactions on BiomedicalEngineering vol 51 no 6 pp 1003ndash1010 2004
[11] J Meng G Huang D Zhang and X Zhu ldquoOptimizing spatialspectral patterns jointly with channel configuration for brain-computer interfacerdquoNeurocomputing vol 104 pp 115ndash126 2013
[12] C Brunner M Naeem and G Pfurtscheller ldquoDimensionalityreduction and channel selection of motor imagery electroen-cephalographic datardquo Computational Intelligence and Neuro-science vol 2009 Article ID 537504 8 pages 2009
[13] H Lu H-L Eng C Guan K N Plataniotis and A NVenetsanopoulos ldquoRegularized common spatial pattern withaggregation for EEG Classification in small-sample settingrdquoIEEE Transactions on Biomedical Engineering vol 57 no 12 pp2936ndash2946 2010
[14] B Wang C M Wong F Wan P U Mak P-I Mak and M IVai ldquoTrial pruning based on genetic algorithm for single-trialEEG classificationrdquo Computers and Electrical Engineering vol38 no 1 pp 35ndash44 2012
[15] B Blankertz S Lemm M Treder S Haufe and K-R MullerldquoSingle-trial analysis and classification of ERP components atutorialrdquo NeuroImage vol 56 no 2 pp 814ndash825 2011
[16] S Siuly and Y Li ldquoImproving the separability of motor imageryEEG signals using a cross correlation-based least square support
vector machine for brain-computer interfacerdquo IEEE Transac-tions on Neural Systems and Rehabilitation Engineering vol 20no 4 pp 526ndash538 2012
[17] A Vuckovic and F Sepulveda ldquoDelta band contribution incue based single trial classification of real and imaginary wristmovementsrdquo Medical and Biological Engineering and Comput-ing vol 46 no 6 pp 529ndash539 2008
[18] K S Lee and Z W Geem ldquoA new meta-heuristic algorithm forcontinuous engineering optimization harmony search theoryand practicerdquo Computer Methods in Applied Mechanics andEngineering vol 194 no 36ndash38 pp 3902ndash3933 2005
[19] X-S Yang ldquoHarmony search as a metaheuristic algorithmrdquoMusic-Inspired Harmony Search Algorithm vol 191 pp 1ndash142009
[20] Z W Geem J H Kim and G V Loganathan ldquoA new heuristicoptimization algorithm harmony searchrdquo Simulation vol 76no 2 pp 60ndash68 2001
[21] Q-K Pan P N Suganthan M F Tasgetiren and J J LiangldquoA self-adaptive global best harmony search algorithm forcontinuous optimization problemsrdquo Applied Mathematics andComputation vol 216 no 3 pp 830ndash848 2010
[22] L D S Coelho and V C Mariani ldquoAn improved harmonysearch algorithm for power economic load dispatchrdquo EnergyConversion and Management vol 50 no 10 pp 2522ndash25262009
[23] Z W Geem and K-B Sim ldquoParameter-setting-free harmonysearch algorithmrdquo Applied Mathematics and Computation vol217 no 8 pp 3881ndash3889 2010
[24] M G Wentrup and M Buss ldquoMulticlass common spatialpatterns and information theoretic feature extractionrdquo IEEETransactions on Biomedical Engineering vol 55 no 8 pp 1991ndash2000 2008
[25] L Wang Y Mao Q Niu and M Fei ldquoA multi-objective binaryharmony search algorithmrdquoAdvances in Swarm Intelligence vol6729 no 2 pp 74ndash81 2011
[26] E Niedermeyer and F L da Silva ldquoEEG recording andoperation of the apparatusrdquo in Electroencephalography BasicPrinciples Clinical Applications and Related Fields LippincottWilliams and Wilkins Baltimore Md USA 2004
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
6 Journal of Applied Mathematics
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
Subject 2
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
s
Figure 4 Results of grouping for Subject 2
symbol on the screen to prepare the subject and a whitesentence ldquopress buttonrdquo appeared on the screen to signal tothe subject to push the button pad
41 Channel Grouping For the channel grouping we usedthe PSF HS algorithm and the MSE method for the objectivefunction In the previous research we used the binary HSand the correlated coefficient However the previous methodexhibited poor performance in terms of computational timeIn this study we used the proposed method to improve thespeed and accuracy The number of iterations of the HS andHMS used in this study was 5000 and 20 respectively Thenumber of trials for one epoch of the HS algorithm was 10We identified the groups by determining the number of timeseach channel was grouped with the other channels All 30experiments for the three subjects were summarized in threeresults in Section 51
42 Feature Classification In the classification we used theHS algorithm with PSF where the HMS was 20 and thenumber of iterations was 50000 The results of channelgrouping which dimension of the signal data decreased wereused for classification The CSP algorithm was used forfeature extraction To match the number of channels we
selected a minimum number of channels among the stimuliIn the CSP procedure we used the data from the given twostimuli and determined the eigenvectors of the filtered signalThe data were classified without using any training method
5 Results and Analysis
51 Channel Grouping The results of the channel groupingare shown in the following Figures 3 4 and 5 and Table 1We searched the group by comparing all cases The resultsfor subject 1 exhibited were distinct The audio stimulusactivated the front of the brain the visual stimulus activatedthe backside of the brain and the motion stimulus activatedthe middle of the brain Generally the functions of the brainare known to be specialized by the locationThe area startingwith ldquoCrdquo includes the vicinity from the Cz channel to theC6 channel which is also known as the motor cortex Thevisual stimulus activated the visual cortex and the back ofthe brain A similar tendency could be observed in the othersubjects All subjects showed the relationship between themotion stimulus and the ldquoCrdquo area Although slight differencesexisted the results agreed with those of the existing researchwhich also explained the visual and audio stimuli Therefore
Journal of Applied Mathematics 7
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
Subject 3
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
Figure 5 Results of grouping for Subject 3
Table 1 Channel group results
Subject Stimuli Channel name
Subject 1Audio FP1 AF3 FT7 FC5 FC3 FC1 FCzVisual P5 P3 P1 Pz P2 P4 PO4Motions C5 C3 C1 Cz C2
Subject 2Audio P5 P3 P1 PzVisual PO7 PO5 PO3 POz O1Motions C5 C3 C1 Cz C2 CP2
Subject 3Audio F7 F5 F3 F1 Fz F2 F4 AF4Visual PO3 POz PO4 PO6 PO8 O1 O2 CB1Motions C5 C3 C1 Cz
we conclude that the results of the grouping are related to thegiven stimuli
Furthermore the similar groupmade by a single stimulusalso could be found in the groups subjected to two or threestimuli including a previous single stimulus Therefore wecan analyze the EEG and the brain by separated stimuliAdditionally the number of channels used in the analysis wasreduced by the proposed method These characteristics canmake the EEG analysis easier
However in some cases determining the relationshipbetween the stimulus and the group was difficult Thisproblem implied two limitations of this work First is thedifficulty in strictly controlling the experimental conditionsincluding the state of the subject A human brain constantlyand unconsciously receives stimuli If an unwanted stimulusis administered the accuracy would worsen The second isthe problem resulting from averaging the signals EEG signalshave a high temporal resolution and the data sampling rateused in this studywas 250HzHowever the groupingmethod
8 Journal of Applied Mathematics
Audio-visual
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 6 Classification accuracy of the audio and visual stimuli
Visual-motion
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 7 Classification accuracy of the visual and motion stimuli
focused on the summation of the MSEs in 1 s Therefore theadvantage of EEG was not well presented
52 Feature Classification We used the HS algorithm forclassification The classification in the feature space wasattempted 10 times for 120 features Figures 6 7 and 8 showthe results of the classification for two stimuli
For Subject 1 the best classification result was 7417and the worst classification result was 55 The averageaccuracy rate was 6283 For Subject 2 the best classificationresult was 7750 and the worst classification result was55 The average accuracy rate was 6461 For Subject3 the best classification result was 7250 and the worstclassification result was 5083 The average accuracy ratewas 6253 The HS algorithm is a heuristic algorithm thusthe results had an accuracy-rate range However without anylearning method or prior information the proposed method
Audio-motion
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 8 Classification accuracy of the audio and motion stimuli
exceeded the decision boundary of the two classes by a rateof approximately 63 Furthermore the classified data werenonlinear high-dimensional data
6 Conclusion
In this study we have built a group to easily analyze EEG sig-nals and proposed an EEG signal classificationmethod basedon HS Signal processing and classification are important asthey determine the accuracy of the entire system Thereforewe focused on solving the problems of a BCI system insignal processing and classification First we employed EEGsignal grouping using theHS algorithm to reduce the channelcomplexity From the EEG signal grouping we did not onlyreduce the channel used in the analysis but also separatelyanalyzed the signal of each stimulus Therefore the compu-tational time and the complexity of the data were reducedThe proposedmethod could be the smartest solution for EEGsignal analysis for problems with large data and dimensionNext we classified the EEG signals using HS We proposed anovel classification method with a metaheuristic algorithmfor nonlinear data such as the EEG signals The proposedmethod also focused on unsupervised classification thus wecould classify the data without training or prior informationWe believe that the proposed two methods could be helpfulin enhancing the BCI implementation and the EEG analysisfield
Acknowledgment
This work was supported by the Mid-Career ResearcherProgram through an NRF Grant funded by the MEST (no2012-0008726)
Journal of Applied Mathematics 9
References
[1] L Bi X Fan and Y Liu ldquoEEG-based brain-controlled mobilerobots a surveyrdquo IEEE Transactions on Human-Machine Sys-tems vol 43 no 2 pp 161ndash176 2013
[2] I Iturrate J M Antelis A Kubler and J Minguez ldquoA nonin-vasive brain-actuated wheelchair based on a P300 neurophysi-ological protocol and automated navigationrdquo IEEE Transactionson Robotics vol 25 no 3 pp 614ndash627 2009
[3] A Frisoli C Loconsole D Leonardis et al ldquoA new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitationin real-world tasksrdquo IEEE Transactions on Systems Man andCybernetics C vol 42 no 6 pp 1169ndash1179 2012
[4] J R Wolpaw ldquoBrain-computer interface research comes of agetraditional assumptions meet emerging realitiesrdquo Journal ofMotor Behavior vol 42 no 6 pp 351ndash353 2010
[5] E E Fetz ldquoReal-time control of a robotic arm by neuronalensemblesrdquoNature Neuroscience vol 2 no 7 pp 583ndash584 1999
[6] P LeVan J Maclaren M Herbst R Sostheim M Zaitsev andJ Hennig ldquoBallistocardiographic artifact removal from simul-taneous EEG-fMRI using an optical motion-tracking systemrdquoNeuroImage vol 75 pp 1ndash11 2013
[7] A Ishikawa H Udagawa YMasuda S Kohno T Amita and YInoue ldquoDevelopment of double density whole brain fNIRSwithEEG system for brain machine interfacerdquo in Proceedings of the33rd Annual International Conference of the IEEE Engineeringin Medicine and Biology Society (EMBS rsquo11) pp 6118ndash6122September 2011
[8] O A P Sosa Y Quijano M Doniz and J E Chong-QueroldquoBCI a historical analysis and technology comparisonrdquo in Pro-ceedings of the Pan American Health Care Exchanges Conference(PAHCE rsquo11) pp 205ndash209 Rio de Janeiro Brazil April 2011
[9] Y O Halchenko S J Hanson and B A Pearlmutter ldquoMulti-modal integration FMRIMRI EEGMEGrdquo inAdvanced ImageProcessing in Magnetic Resonance Imaging CRC Press BocaRaton Fla USA 2005
[10] T N Lal M Schroder T Hinterberger et al ldquoSupport vectorchannel selection in BCIrdquo IEEE Transactions on BiomedicalEngineering vol 51 no 6 pp 1003ndash1010 2004
[11] J Meng G Huang D Zhang and X Zhu ldquoOptimizing spatialspectral patterns jointly with channel configuration for brain-computer interfacerdquoNeurocomputing vol 104 pp 115ndash126 2013
[12] C Brunner M Naeem and G Pfurtscheller ldquoDimensionalityreduction and channel selection of motor imagery electroen-cephalographic datardquo Computational Intelligence and Neuro-science vol 2009 Article ID 537504 8 pages 2009
[13] H Lu H-L Eng C Guan K N Plataniotis and A NVenetsanopoulos ldquoRegularized common spatial pattern withaggregation for EEG Classification in small-sample settingrdquoIEEE Transactions on Biomedical Engineering vol 57 no 12 pp2936ndash2946 2010
[14] B Wang C M Wong F Wan P U Mak P-I Mak and M IVai ldquoTrial pruning based on genetic algorithm for single-trialEEG classificationrdquo Computers and Electrical Engineering vol38 no 1 pp 35ndash44 2012
[15] B Blankertz S Lemm M Treder S Haufe and K-R MullerldquoSingle-trial analysis and classification of ERP components atutorialrdquo NeuroImage vol 56 no 2 pp 814ndash825 2011
[16] S Siuly and Y Li ldquoImproving the separability of motor imageryEEG signals using a cross correlation-based least square support
vector machine for brain-computer interfacerdquo IEEE Transac-tions on Neural Systems and Rehabilitation Engineering vol 20no 4 pp 526ndash538 2012
[17] A Vuckovic and F Sepulveda ldquoDelta band contribution incue based single trial classification of real and imaginary wristmovementsrdquo Medical and Biological Engineering and Comput-ing vol 46 no 6 pp 529ndash539 2008
[18] K S Lee and Z W Geem ldquoA new meta-heuristic algorithm forcontinuous engineering optimization harmony search theoryand practicerdquo Computer Methods in Applied Mechanics andEngineering vol 194 no 36ndash38 pp 3902ndash3933 2005
[19] X-S Yang ldquoHarmony search as a metaheuristic algorithmrdquoMusic-Inspired Harmony Search Algorithm vol 191 pp 1ndash142009
[20] Z W Geem J H Kim and G V Loganathan ldquoA new heuristicoptimization algorithm harmony searchrdquo Simulation vol 76no 2 pp 60ndash68 2001
[21] Q-K Pan P N Suganthan M F Tasgetiren and J J LiangldquoA self-adaptive global best harmony search algorithm forcontinuous optimization problemsrdquo Applied Mathematics andComputation vol 216 no 3 pp 830ndash848 2010
[22] L D S Coelho and V C Mariani ldquoAn improved harmonysearch algorithm for power economic load dispatchrdquo EnergyConversion and Management vol 50 no 10 pp 2522ndash25262009
[23] Z W Geem and K-B Sim ldquoParameter-setting-free harmonysearch algorithmrdquo Applied Mathematics and Computation vol217 no 8 pp 3881ndash3889 2010
[24] M G Wentrup and M Buss ldquoMulticlass common spatialpatterns and information theoretic feature extractionrdquo IEEETransactions on Biomedical Engineering vol 55 no 8 pp 1991ndash2000 2008
[25] L Wang Y Mao Q Niu and M Fei ldquoA multi-objective binaryharmony search algorithmrdquoAdvances in Swarm Intelligence vol6729 no 2 pp 74ndash81 2011
[26] E Niedermeyer and F L da Silva ldquoEEG recording andoperation of the apparatusrdquo in Electroencephalography BasicPrinciples Clinical Applications and Related Fields LippincottWilliams and Wilkins Baltimore Md USA 2004
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Journal of Applied Mathematics 7
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
minus5 0 5minus5
0
5
Subject 3
Sound Visual Movement
Sound visual
All stimuli
Sound movement Visual movement
Figure 5 Results of grouping for Subject 3
Table 1 Channel group results
Subject Stimuli Channel name
Subject 1Audio FP1 AF3 FT7 FC5 FC3 FC1 FCzVisual P5 P3 P1 Pz P2 P4 PO4Motions C5 C3 C1 Cz C2
Subject 2Audio P5 P3 P1 PzVisual PO7 PO5 PO3 POz O1Motions C5 C3 C1 Cz C2 CP2
Subject 3Audio F7 F5 F3 F1 Fz F2 F4 AF4Visual PO3 POz PO4 PO6 PO8 O1 O2 CB1Motions C5 C3 C1 Cz
we conclude that the results of the grouping are related to thegiven stimuli
Furthermore the similar groupmade by a single stimulusalso could be found in the groups subjected to two or threestimuli including a previous single stimulus Therefore wecan analyze the EEG and the brain by separated stimuliAdditionally the number of channels used in the analysis wasreduced by the proposed method These characteristics canmake the EEG analysis easier
However in some cases determining the relationshipbetween the stimulus and the group was difficult Thisproblem implied two limitations of this work First is thedifficulty in strictly controlling the experimental conditionsincluding the state of the subject A human brain constantlyand unconsciously receives stimuli If an unwanted stimulusis administered the accuracy would worsen The second isthe problem resulting from averaging the signals EEG signalshave a high temporal resolution and the data sampling rateused in this studywas 250HzHowever the groupingmethod
8 Journal of Applied Mathematics
Audio-visual
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 6 Classification accuracy of the audio and visual stimuli
Visual-motion
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 7 Classification accuracy of the visual and motion stimuli
focused on the summation of the MSEs in 1 s Therefore theadvantage of EEG was not well presented
52 Feature Classification We used the HS algorithm forclassification The classification in the feature space wasattempted 10 times for 120 features Figures 6 7 and 8 showthe results of the classification for two stimuli
For Subject 1 the best classification result was 7417and the worst classification result was 55 The averageaccuracy rate was 6283 For Subject 2 the best classificationresult was 7750 and the worst classification result was55 The average accuracy rate was 6461 For Subject3 the best classification result was 7250 and the worstclassification result was 5083 The average accuracy ratewas 6253 The HS algorithm is a heuristic algorithm thusthe results had an accuracy-rate range However without anylearning method or prior information the proposed method
Audio-motion
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 8 Classification accuracy of the audio and motion stimuli
exceeded the decision boundary of the two classes by a rateof approximately 63 Furthermore the classified data werenonlinear high-dimensional data
6 Conclusion
In this study we have built a group to easily analyze EEG sig-nals and proposed an EEG signal classificationmethod basedon HS Signal processing and classification are important asthey determine the accuracy of the entire system Thereforewe focused on solving the problems of a BCI system insignal processing and classification First we employed EEGsignal grouping using theHS algorithm to reduce the channelcomplexity From the EEG signal grouping we did not onlyreduce the channel used in the analysis but also separatelyanalyzed the signal of each stimulus Therefore the compu-tational time and the complexity of the data were reducedThe proposedmethod could be the smartest solution for EEGsignal analysis for problems with large data and dimensionNext we classified the EEG signals using HS We proposed anovel classification method with a metaheuristic algorithmfor nonlinear data such as the EEG signals The proposedmethod also focused on unsupervised classification thus wecould classify the data without training or prior informationWe believe that the proposed two methods could be helpfulin enhancing the BCI implementation and the EEG analysisfield
Acknowledgment
This work was supported by the Mid-Career ResearcherProgram through an NRF Grant funded by the MEST (no2012-0008726)
Journal of Applied Mathematics 9
References
[1] L Bi X Fan and Y Liu ldquoEEG-based brain-controlled mobilerobots a surveyrdquo IEEE Transactions on Human-Machine Sys-tems vol 43 no 2 pp 161ndash176 2013
[2] I Iturrate J M Antelis A Kubler and J Minguez ldquoA nonin-vasive brain-actuated wheelchair based on a P300 neurophysi-ological protocol and automated navigationrdquo IEEE Transactionson Robotics vol 25 no 3 pp 614ndash627 2009
[3] A Frisoli C Loconsole D Leonardis et al ldquoA new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitationin real-world tasksrdquo IEEE Transactions on Systems Man andCybernetics C vol 42 no 6 pp 1169ndash1179 2012
[4] J R Wolpaw ldquoBrain-computer interface research comes of agetraditional assumptions meet emerging realitiesrdquo Journal ofMotor Behavior vol 42 no 6 pp 351ndash353 2010
[5] E E Fetz ldquoReal-time control of a robotic arm by neuronalensemblesrdquoNature Neuroscience vol 2 no 7 pp 583ndash584 1999
[6] P LeVan J Maclaren M Herbst R Sostheim M Zaitsev andJ Hennig ldquoBallistocardiographic artifact removal from simul-taneous EEG-fMRI using an optical motion-tracking systemrdquoNeuroImage vol 75 pp 1ndash11 2013
[7] A Ishikawa H Udagawa YMasuda S Kohno T Amita and YInoue ldquoDevelopment of double density whole brain fNIRSwithEEG system for brain machine interfacerdquo in Proceedings of the33rd Annual International Conference of the IEEE Engineeringin Medicine and Biology Society (EMBS rsquo11) pp 6118ndash6122September 2011
[8] O A P Sosa Y Quijano M Doniz and J E Chong-QueroldquoBCI a historical analysis and technology comparisonrdquo in Pro-ceedings of the Pan American Health Care Exchanges Conference(PAHCE rsquo11) pp 205ndash209 Rio de Janeiro Brazil April 2011
[9] Y O Halchenko S J Hanson and B A Pearlmutter ldquoMulti-modal integration FMRIMRI EEGMEGrdquo inAdvanced ImageProcessing in Magnetic Resonance Imaging CRC Press BocaRaton Fla USA 2005
[10] T N Lal M Schroder T Hinterberger et al ldquoSupport vectorchannel selection in BCIrdquo IEEE Transactions on BiomedicalEngineering vol 51 no 6 pp 1003ndash1010 2004
[11] J Meng G Huang D Zhang and X Zhu ldquoOptimizing spatialspectral patterns jointly with channel configuration for brain-computer interfacerdquoNeurocomputing vol 104 pp 115ndash126 2013
[12] C Brunner M Naeem and G Pfurtscheller ldquoDimensionalityreduction and channel selection of motor imagery electroen-cephalographic datardquo Computational Intelligence and Neuro-science vol 2009 Article ID 537504 8 pages 2009
[13] H Lu H-L Eng C Guan K N Plataniotis and A NVenetsanopoulos ldquoRegularized common spatial pattern withaggregation for EEG Classification in small-sample settingrdquoIEEE Transactions on Biomedical Engineering vol 57 no 12 pp2936ndash2946 2010
[14] B Wang C M Wong F Wan P U Mak P-I Mak and M IVai ldquoTrial pruning based on genetic algorithm for single-trialEEG classificationrdquo Computers and Electrical Engineering vol38 no 1 pp 35ndash44 2012
[15] B Blankertz S Lemm M Treder S Haufe and K-R MullerldquoSingle-trial analysis and classification of ERP components atutorialrdquo NeuroImage vol 56 no 2 pp 814ndash825 2011
[16] S Siuly and Y Li ldquoImproving the separability of motor imageryEEG signals using a cross correlation-based least square support
vector machine for brain-computer interfacerdquo IEEE Transac-tions on Neural Systems and Rehabilitation Engineering vol 20no 4 pp 526ndash538 2012
[17] A Vuckovic and F Sepulveda ldquoDelta band contribution incue based single trial classification of real and imaginary wristmovementsrdquo Medical and Biological Engineering and Comput-ing vol 46 no 6 pp 529ndash539 2008
[18] K S Lee and Z W Geem ldquoA new meta-heuristic algorithm forcontinuous engineering optimization harmony search theoryand practicerdquo Computer Methods in Applied Mechanics andEngineering vol 194 no 36ndash38 pp 3902ndash3933 2005
[19] X-S Yang ldquoHarmony search as a metaheuristic algorithmrdquoMusic-Inspired Harmony Search Algorithm vol 191 pp 1ndash142009
[20] Z W Geem J H Kim and G V Loganathan ldquoA new heuristicoptimization algorithm harmony searchrdquo Simulation vol 76no 2 pp 60ndash68 2001
[21] Q-K Pan P N Suganthan M F Tasgetiren and J J LiangldquoA self-adaptive global best harmony search algorithm forcontinuous optimization problemsrdquo Applied Mathematics andComputation vol 216 no 3 pp 830ndash848 2010
[22] L D S Coelho and V C Mariani ldquoAn improved harmonysearch algorithm for power economic load dispatchrdquo EnergyConversion and Management vol 50 no 10 pp 2522ndash25262009
[23] Z W Geem and K-B Sim ldquoParameter-setting-free harmonysearch algorithmrdquo Applied Mathematics and Computation vol217 no 8 pp 3881ndash3889 2010
[24] M G Wentrup and M Buss ldquoMulticlass common spatialpatterns and information theoretic feature extractionrdquo IEEETransactions on Biomedical Engineering vol 55 no 8 pp 1991ndash2000 2008
[25] L Wang Y Mao Q Niu and M Fei ldquoA multi-objective binaryharmony search algorithmrdquoAdvances in Swarm Intelligence vol6729 no 2 pp 74ndash81 2011
[26] E Niedermeyer and F L da Silva ldquoEEG recording andoperation of the apparatusrdquo in Electroencephalography BasicPrinciples Clinical Applications and Related Fields LippincottWilliams and Wilkins Baltimore Md USA 2004
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
8 Journal of Applied Mathematics
Audio-visual
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 6 Classification accuracy of the audio and visual stimuli
Visual-motion
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 7 Classification accuracy of the visual and motion stimuli
focused on the summation of the MSEs in 1 s Therefore theadvantage of EEG was not well presented
52 Feature Classification We used the HS algorithm forclassification The classification in the feature space wasattempted 10 times for 120 features Figures 6 7 and 8 showthe results of the classification for two stimuli
For Subject 1 the best classification result was 7417and the worst classification result was 55 The averageaccuracy rate was 6283 For Subject 2 the best classificationresult was 7750 and the worst classification result was55 The average accuracy rate was 6461 For Subject3 the best classification result was 7250 and the worstclassification result was 5083 The average accuracy ratewas 6253 The HS algorithm is a heuristic algorithm thusthe results had an accuracy-rate range However without anylearning method or prior information the proposed method
Audio-motion
Accu
racy
()
1 2 3 4 5 6 7 8 9 10Trial
Subject 1Subject 2Subject 3
45505560657075808590
Figure 8 Classification accuracy of the audio and motion stimuli
exceeded the decision boundary of the two classes by a rateof approximately 63 Furthermore the classified data werenonlinear high-dimensional data
6 Conclusion
In this study we have built a group to easily analyze EEG sig-nals and proposed an EEG signal classificationmethod basedon HS Signal processing and classification are important asthey determine the accuracy of the entire system Thereforewe focused on solving the problems of a BCI system insignal processing and classification First we employed EEGsignal grouping using theHS algorithm to reduce the channelcomplexity From the EEG signal grouping we did not onlyreduce the channel used in the analysis but also separatelyanalyzed the signal of each stimulus Therefore the compu-tational time and the complexity of the data were reducedThe proposedmethod could be the smartest solution for EEGsignal analysis for problems with large data and dimensionNext we classified the EEG signals using HS We proposed anovel classification method with a metaheuristic algorithmfor nonlinear data such as the EEG signals The proposedmethod also focused on unsupervised classification thus wecould classify the data without training or prior informationWe believe that the proposed two methods could be helpfulin enhancing the BCI implementation and the EEG analysisfield
Acknowledgment
This work was supported by the Mid-Career ResearcherProgram through an NRF Grant funded by the MEST (no2012-0008726)
Journal of Applied Mathematics 9
References
[1] L Bi X Fan and Y Liu ldquoEEG-based brain-controlled mobilerobots a surveyrdquo IEEE Transactions on Human-Machine Sys-tems vol 43 no 2 pp 161ndash176 2013
[2] I Iturrate J M Antelis A Kubler and J Minguez ldquoA nonin-vasive brain-actuated wheelchair based on a P300 neurophysi-ological protocol and automated navigationrdquo IEEE Transactionson Robotics vol 25 no 3 pp 614ndash627 2009
[3] A Frisoli C Loconsole D Leonardis et al ldquoA new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitationin real-world tasksrdquo IEEE Transactions on Systems Man andCybernetics C vol 42 no 6 pp 1169ndash1179 2012
[4] J R Wolpaw ldquoBrain-computer interface research comes of agetraditional assumptions meet emerging realitiesrdquo Journal ofMotor Behavior vol 42 no 6 pp 351ndash353 2010
[5] E E Fetz ldquoReal-time control of a robotic arm by neuronalensemblesrdquoNature Neuroscience vol 2 no 7 pp 583ndash584 1999
[6] P LeVan J Maclaren M Herbst R Sostheim M Zaitsev andJ Hennig ldquoBallistocardiographic artifact removal from simul-taneous EEG-fMRI using an optical motion-tracking systemrdquoNeuroImage vol 75 pp 1ndash11 2013
[7] A Ishikawa H Udagawa YMasuda S Kohno T Amita and YInoue ldquoDevelopment of double density whole brain fNIRSwithEEG system for brain machine interfacerdquo in Proceedings of the33rd Annual International Conference of the IEEE Engineeringin Medicine and Biology Society (EMBS rsquo11) pp 6118ndash6122September 2011
[8] O A P Sosa Y Quijano M Doniz and J E Chong-QueroldquoBCI a historical analysis and technology comparisonrdquo in Pro-ceedings of the Pan American Health Care Exchanges Conference(PAHCE rsquo11) pp 205ndash209 Rio de Janeiro Brazil April 2011
[9] Y O Halchenko S J Hanson and B A Pearlmutter ldquoMulti-modal integration FMRIMRI EEGMEGrdquo inAdvanced ImageProcessing in Magnetic Resonance Imaging CRC Press BocaRaton Fla USA 2005
[10] T N Lal M Schroder T Hinterberger et al ldquoSupport vectorchannel selection in BCIrdquo IEEE Transactions on BiomedicalEngineering vol 51 no 6 pp 1003ndash1010 2004
[11] J Meng G Huang D Zhang and X Zhu ldquoOptimizing spatialspectral patterns jointly with channel configuration for brain-computer interfacerdquoNeurocomputing vol 104 pp 115ndash126 2013
[12] C Brunner M Naeem and G Pfurtscheller ldquoDimensionalityreduction and channel selection of motor imagery electroen-cephalographic datardquo Computational Intelligence and Neuro-science vol 2009 Article ID 537504 8 pages 2009
[13] H Lu H-L Eng C Guan K N Plataniotis and A NVenetsanopoulos ldquoRegularized common spatial pattern withaggregation for EEG Classification in small-sample settingrdquoIEEE Transactions on Biomedical Engineering vol 57 no 12 pp2936ndash2946 2010
[14] B Wang C M Wong F Wan P U Mak P-I Mak and M IVai ldquoTrial pruning based on genetic algorithm for single-trialEEG classificationrdquo Computers and Electrical Engineering vol38 no 1 pp 35ndash44 2012
[15] B Blankertz S Lemm M Treder S Haufe and K-R MullerldquoSingle-trial analysis and classification of ERP components atutorialrdquo NeuroImage vol 56 no 2 pp 814ndash825 2011
[16] S Siuly and Y Li ldquoImproving the separability of motor imageryEEG signals using a cross correlation-based least square support
vector machine for brain-computer interfacerdquo IEEE Transac-tions on Neural Systems and Rehabilitation Engineering vol 20no 4 pp 526ndash538 2012
[17] A Vuckovic and F Sepulveda ldquoDelta band contribution incue based single trial classification of real and imaginary wristmovementsrdquo Medical and Biological Engineering and Comput-ing vol 46 no 6 pp 529ndash539 2008
[18] K S Lee and Z W Geem ldquoA new meta-heuristic algorithm forcontinuous engineering optimization harmony search theoryand practicerdquo Computer Methods in Applied Mechanics andEngineering vol 194 no 36ndash38 pp 3902ndash3933 2005
[19] X-S Yang ldquoHarmony search as a metaheuristic algorithmrdquoMusic-Inspired Harmony Search Algorithm vol 191 pp 1ndash142009
[20] Z W Geem J H Kim and G V Loganathan ldquoA new heuristicoptimization algorithm harmony searchrdquo Simulation vol 76no 2 pp 60ndash68 2001
[21] Q-K Pan P N Suganthan M F Tasgetiren and J J LiangldquoA self-adaptive global best harmony search algorithm forcontinuous optimization problemsrdquo Applied Mathematics andComputation vol 216 no 3 pp 830ndash848 2010
[22] L D S Coelho and V C Mariani ldquoAn improved harmonysearch algorithm for power economic load dispatchrdquo EnergyConversion and Management vol 50 no 10 pp 2522ndash25262009
[23] Z W Geem and K-B Sim ldquoParameter-setting-free harmonysearch algorithmrdquo Applied Mathematics and Computation vol217 no 8 pp 3881ndash3889 2010
[24] M G Wentrup and M Buss ldquoMulticlass common spatialpatterns and information theoretic feature extractionrdquo IEEETransactions on Biomedical Engineering vol 55 no 8 pp 1991ndash2000 2008
[25] L Wang Y Mao Q Niu and M Fei ldquoA multi-objective binaryharmony search algorithmrdquoAdvances in Swarm Intelligence vol6729 no 2 pp 74ndash81 2011
[26] E Niedermeyer and F L da Silva ldquoEEG recording andoperation of the apparatusrdquo in Electroencephalography BasicPrinciples Clinical Applications and Related Fields LippincottWilliams and Wilkins Baltimore Md USA 2004
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Journal of Applied Mathematics 9
References
[1] L Bi X Fan and Y Liu ldquoEEG-based brain-controlled mobilerobots a surveyrdquo IEEE Transactions on Human-Machine Sys-tems vol 43 no 2 pp 161ndash176 2013
[2] I Iturrate J M Antelis A Kubler and J Minguez ldquoA nonin-vasive brain-actuated wheelchair based on a P300 neurophysi-ological protocol and automated navigationrdquo IEEE Transactionson Robotics vol 25 no 3 pp 614ndash627 2009
[3] A Frisoli C Loconsole D Leonardis et al ldquoA new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitationin real-world tasksrdquo IEEE Transactions on Systems Man andCybernetics C vol 42 no 6 pp 1169ndash1179 2012
[4] J R Wolpaw ldquoBrain-computer interface research comes of agetraditional assumptions meet emerging realitiesrdquo Journal ofMotor Behavior vol 42 no 6 pp 351ndash353 2010
[5] E E Fetz ldquoReal-time control of a robotic arm by neuronalensemblesrdquoNature Neuroscience vol 2 no 7 pp 583ndash584 1999
[6] P LeVan J Maclaren M Herbst R Sostheim M Zaitsev andJ Hennig ldquoBallistocardiographic artifact removal from simul-taneous EEG-fMRI using an optical motion-tracking systemrdquoNeuroImage vol 75 pp 1ndash11 2013
[7] A Ishikawa H Udagawa YMasuda S Kohno T Amita and YInoue ldquoDevelopment of double density whole brain fNIRSwithEEG system for brain machine interfacerdquo in Proceedings of the33rd Annual International Conference of the IEEE Engineeringin Medicine and Biology Society (EMBS rsquo11) pp 6118ndash6122September 2011
[8] O A P Sosa Y Quijano M Doniz and J E Chong-QueroldquoBCI a historical analysis and technology comparisonrdquo in Pro-ceedings of the Pan American Health Care Exchanges Conference(PAHCE rsquo11) pp 205ndash209 Rio de Janeiro Brazil April 2011
[9] Y O Halchenko S J Hanson and B A Pearlmutter ldquoMulti-modal integration FMRIMRI EEGMEGrdquo inAdvanced ImageProcessing in Magnetic Resonance Imaging CRC Press BocaRaton Fla USA 2005
[10] T N Lal M Schroder T Hinterberger et al ldquoSupport vectorchannel selection in BCIrdquo IEEE Transactions on BiomedicalEngineering vol 51 no 6 pp 1003ndash1010 2004
[11] J Meng G Huang D Zhang and X Zhu ldquoOptimizing spatialspectral patterns jointly with channel configuration for brain-computer interfacerdquoNeurocomputing vol 104 pp 115ndash126 2013
[12] C Brunner M Naeem and G Pfurtscheller ldquoDimensionalityreduction and channel selection of motor imagery electroen-cephalographic datardquo Computational Intelligence and Neuro-science vol 2009 Article ID 537504 8 pages 2009
[13] H Lu H-L Eng C Guan K N Plataniotis and A NVenetsanopoulos ldquoRegularized common spatial pattern withaggregation for EEG Classification in small-sample settingrdquoIEEE Transactions on Biomedical Engineering vol 57 no 12 pp2936ndash2946 2010
[14] B Wang C M Wong F Wan P U Mak P-I Mak and M IVai ldquoTrial pruning based on genetic algorithm for single-trialEEG classificationrdquo Computers and Electrical Engineering vol38 no 1 pp 35ndash44 2012
[15] B Blankertz S Lemm M Treder S Haufe and K-R MullerldquoSingle-trial analysis and classification of ERP components atutorialrdquo NeuroImage vol 56 no 2 pp 814ndash825 2011
[16] S Siuly and Y Li ldquoImproving the separability of motor imageryEEG signals using a cross correlation-based least square support
vector machine for brain-computer interfacerdquo IEEE Transac-tions on Neural Systems and Rehabilitation Engineering vol 20no 4 pp 526ndash538 2012
[17] A Vuckovic and F Sepulveda ldquoDelta band contribution incue based single trial classification of real and imaginary wristmovementsrdquo Medical and Biological Engineering and Comput-ing vol 46 no 6 pp 529ndash539 2008
[18] K S Lee and Z W Geem ldquoA new meta-heuristic algorithm forcontinuous engineering optimization harmony search theoryand practicerdquo Computer Methods in Applied Mechanics andEngineering vol 194 no 36ndash38 pp 3902ndash3933 2005
[19] X-S Yang ldquoHarmony search as a metaheuristic algorithmrdquoMusic-Inspired Harmony Search Algorithm vol 191 pp 1ndash142009
[20] Z W Geem J H Kim and G V Loganathan ldquoA new heuristicoptimization algorithm harmony searchrdquo Simulation vol 76no 2 pp 60ndash68 2001
[21] Q-K Pan P N Suganthan M F Tasgetiren and J J LiangldquoA self-adaptive global best harmony search algorithm forcontinuous optimization problemsrdquo Applied Mathematics andComputation vol 216 no 3 pp 830ndash848 2010
[22] L D S Coelho and V C Mariani ldquoAn improved harmonysearch algorithm for power economic load dispatchrdquo EnergyConversion and Management vol 50 no 10 pp 2522ndash25262009
[23] Z W Geem and K-B Sim ldquoParameter-setting-free harmonysearch algorithmrdquo Applied Mathematics and Computation vol217 no 8 pp 3881ndash3889 2010
[24] M G Wentrup and M Buss ldquoMulticlass common spatialpatterns and information theoretic feature extractionrdquo IEEETransactions on Biomedical Engineering vol 55 no 8 pp 1991ndash2000 2008
[25] L Wang Y Mao Q Niu and M Fei ldquoA multi-objective binaryharmony search algorithmrdquoAdvances in Swarm Intelligence vol6729 no 2 pp 74ndash81 2011
[26] E Niedermeyer and F L da Silva ldquoEEG recording andoperation of the apparatusrdquo in Electroencephalography BasicPrinciples Clinical Applications and Related Fields LippincottWilliams and Wilkins Baltimore Md USA 2004
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
