EEG-Based Brain Computer Interface Systemfor Cursor Control Velocity Regression

with Recurrent Neural Network

Haoqi WANG

the Hong Kong University of Science and Technologyhwangby@connect.ust.hk

Abstract— This paper presents the decodingof EEG signal using recurrent neural networkto predict the movement velocity of computercursor.

I. INTRODUCTIONA. BACKGROUNDBCI

Brain Computer Interface (BCI) com-bines the techniques of brain activityrecording, feature extraction and process-ing, and computer commands interpreta-tion [1]. BCI is now being explored inmany applications, including but not lim-ited to human augmentation, lie detection,security and authentication. [2] There aretwo general categories of BCI, dependingon the brain signal acquisition methods:invasive and non-invasive methods [3].

Invasive recording approaches implantelectrodes under the scalp. The neural ac-tivity of the brain is measured either on thecortical surface or from within the motorcortex intracortically. The most significantadvantage is that they provide high tem-poral and spatial resolution, increasing thequality of the obtained signal and its signalto noise ratio [4].

However, these techniques suffer fromplenty of issues. Aside from usability is-sues rising from the involvement of sur-gical procedure, problems related to thesystems output have occurred. The smallsize of the monitored brain regions by

those implants is considered one of them.Once implanted, they cannot be shiftedto measure brain activity in another area.Besides, the body adaptation to the newobject, which may fail, can cause medi-cal complications. Problems regarding thestability of implants and protection frominfection can arise as well. Thus the usageof invasive recording in real world hasbeen usually restricted to the BCI basedmedical applications for a few disabledusers [5].

Noninvasive methods do not require ex-ternal object implantation into the brainof the subject. Thus it avoids the surgi-cal procedures or permanent device at-tachment needed by invasive acquisition.Various assessment methods for differ-ent types of measured signals exist suchas functional magnetic resonance imag-ing (fMRI), functional nearinfrared spec-troscopy (fNIRS), magnetoencephalogra-phy (MEG), and electroencephalogram(EEG) [4].

EEG

As a non-invasive BCI measurementtechnique, EEG provides the brain elec-trical activity recording from the surfaceof the scalp, where electrodes are placedon the scalp to pickup the electrical cur-rent generated by the brain [6]. EEG datacontains rhythmic activity, which reflectsneural oscillations, which are described by

frequency, power and phase. Such oscilla-tions occur at certain frequencies. Theseinclude delta, alpha, gamma, theta andmeta. Research has found correlations be-tween these rhythms and different brainstates [7]. For instance, alpha frequencyof the brain activity is often captured bycommercial EEG headsets for measuringthe degree of meditation.

B. EXPERIMENT SETTINGS AND DATA

In this experiment, the subjects wereasked to track the moving 2-D cursor onthe screen by thinking that they were usingtheir dominant hand to control the cursormoving the same way as it was on thescreen. In the mean time, the EEG signalis recorded wirelessly with Emotiv EPOCheadset of 14 channels. The headset withhydrated electrodes was put on the scalpof the subject. TestBench, a Emotiv soft-ware was used to ensure the signal qualityduring the recording process. Meanwhile,the signal was processed by a high passfilter at 0.16Hz and a low pass filter at30 Hz. 14 channel EEG data and cursormovement were recorded simultaneouslyat a sample rate of 128Hz. The data arestored by BCI2000 software.

There are ten trails in total for eachsubject. The cursor moves along the hori-zontal axis for five trails and along the ver-tical axis for another five trails. Each traillasts for 60 seconds. The cursor movesforward and backward repeatedly, but it isnot strictly periodic.

C. RELATED STUDY

Research has been conducted to studythe control of a computer cursor usingsignals from imagination of the movementof body parts [8] in 1-D [9], 2-D [10] [11]and 3-D [12]. Different command signalsin the cursor control task are mapped bycorresponding sensorimotor rhythm [13]changes from different mental states. Be-sides the sensorimotor approach to con-trol a cursor, some researches studied

the hybrid EEG paradigm for the cursorcontrol. A target practice brain computerinterface system based on mental stateswas used to control 1-D cursor in 2006[14]. Later, Steady State Visual EvokedPotential method was implemented on 2-Dcursor control problem [15]. In 2010, theP300 potential and mental states were usedin the 2-D cursor control problem [16].

One of the drawbacks of the noninvasivebrain computer interface systems in 2-D or3-D cursor control mentioned above is thatit requires weeks to months for training togain a satisfied performance. Subjects needsuch lengthy time to learn to modulatecertain frequency bands during a neuralactivity in the process of moving the cursorto a specific target. Moreover, it shouldbe noted that the fatigue phenomenon re-ported by some researchers and subjectsinfluences the cursor control experiments.Also, the discrete control of cursor direc-tions caused by switching imaginary bodyparts is also an issue of these paradigms.

A new noninvasive EEG-based methodof 2-D cursor control was introduced usinga way similar to invasive devices thatcan minimize the training time. Positiveperformance can be attained just after ap-proximately forty minutes of training. Thesubjects with electrodes implanted in theirbrains can reach high success rates in tar-get acquisition with continuous imaginedkinematics of one body part. [17] Thesestudies show that natural imagined bodykinematics paradigm can greatly reducethe training time and can give insight togeneric model that requires no training.

In the previous study, Multiple LinearRegression decoder model was used topredict the velocity of the computer cursor.In this project, Recurrent Neural Networkmodels is implemented for the cursor ve-locity regression.

II. METHOD

In this project, there are three majorsteps for EEG signal decoding. Firstly, to

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acquire the useful band frequency data, theraw EEG signals pass through a low passButterworth filter. Secondly, the filtereddata are fed into two kinds of recurrentneural network. One consists of long shortterm memory (LSTM) cells. The otheris made of gated recurrent unit (GRU)cells. Thirdly, the prediction results arecompared to the real cursor movement datato evaluate the effectiveness of the model.There are two types of score for the effec-tiveness calculation, R squared value andcorrelation goodness of fit. Three thingswill be discussed in this paper, the per-formance between LSTM model and GRUmodels, the validity of the RNN models,and the effectiveness of the recurrent neu-ral network model to all the subjects.

A. FILTERINGThe Butterworth filter is a type of signal

processing filter designed to have a fre-quency response as flat as possible in thepassband.[18] A 4th order low pass Butter-worth filter of 2Hz cut-off frequency wasused. The main purpose of applying a lowpass filter is to reduce the high frequencyfluctuations for more smooth input signalof the neural network. According to someliterature [19], the EEG signal under 2Hzhas a close relation with the cursor control.

B. RECURRENT NEURAL NETWORKSA recurrent neural network (RNN) is

a class of artificial neural network whereconnections between nodes form a di-rected graph along a sequence. This allowsit to exhibit dynamic temporal behaviorfor a time sequence. Unlike feedforwardneural networks, RNNs can use their in-ternal state (memory) to process sequencesof inputs [20]. This makes them applicableto tasks in this experiment since our inputdata is a sequence of time.

Two models were implemented sepa-rately for this process. The Long ShortTerm Memory (LSTM) model is com-posed of LSTM units which have cells,

Fig. 1. Butterworth filter result.

Fig. 2. Butterworth filter result.

Fig. 3. Butterworth filter result.

Fig. 4. Butterworth filter result.

input gates, output gates and forget gates.The Gated Recurrent Unit (GRU) consistsof update gates and reset gates. The datafor each trail lasts for 60 seconds, witha sample frequency of 128Hz, which is7680 samples in total. The correspondingcursor movement data is also 128Hz for 60seconds. The result comparison and effec-tiveness of the two models are discussedbelow.

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C. CROSS VALIDATIONLeave One Out cross validation method

is used to prevent the model over fittingproblem. Since there are five trails for eachregression model, one trail was left outfor validation while the other four trailswere for training. Therefore, five modelsare generated according to five differenttraining and testing combinations.

D. EVALUATIONThe models were evaluated using two

types of score called Goodness-of-Fit(GoF). This scoring technique separatedthe trial into segments of 5 seconds. Thefirst type is to average the Pearson correla-tion scores between the predicted and ac-tual cursor velocities. Then, the averagedvalue of the Pearson correlation scoresover each trial was defined as the GoF. Thesecond type is to average the R squaredvalue scores between the predicted andactual cursor velocities. These methodscan provide a better representation of fitby not allowing one improperly fit windowto reduce the overall models score.

GoF1 =1M

M

∑i=1

Corr(V idecoded,V

iobserved)

GoF2 =1M

M

∑i=1

R2(V idecoded,V

iobserved)

where Vdecoded and Vobserved representsthe decoded velocity and the observedvelocity for the ith segment, respectively.The two kinds of scores are of similartrends in terms of the evaluation of themodel goodness. Therefore, the correlationscore will be further illustrated next as anexample.

III. RESULTSFrom the tables in the appendix, it is

shown that the LSTM and the GRU modelhave very similar performance and accu-racy. In a random seeded machine learningprocess, the highest correlation mark of

the LSTM horizontal regression is 0.8425.The correlation mark of the GRU horizon-tal regression can reach up to 0.8203. Thebest vertical regression of LSTM scoreis 0.7695. And the vertical regression ofGRU model goes up to 0.7113. The ta-ble also reveals that the horizontal cursormovement prediction is generally betterthan the vertical cursor movement predic-tion for the same subject. It is worth notingthat the prediction accuracy varies fromsubject to subject. For example, subject25 performs very well in both horizontaland vertical LSTM trails with an averagescore of 0.7692 for horizontal trails andan average score of 0.5655 for verticaltrails. Subject 21 performs well in thehorizontal tests but poorly in the verticaltests. Subject 14 can perform equally wellresults on both types. However, there aresubjects who have no significant patternssuch as subject 16.

The pictures next page are some pre-diction result using LSTM neural network.The red line is the actual cursor movementafter normalization. The blue line is theprediction result.

IV. CONCLUSIONS

In this project, recurrent neural net-work was implemented in the form ofLSTM and GRU respectively, and the per-formance of the two models has greatlyimproved. Interesting conclusions of thesubject based EEG decoding interpretationwere drawn. It is shown that the RNNis an effective method for EEG decoding.Though many factors contribute to affect-ing the accuracy of the model, such asthe choice of the recurrent neural networkcells, the filter frequency, and the controlparameters, the most determinant variableis the subject itself.

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Fig. 5. Horizontal Trail.

Fig. 6. Horizontal Trail.

Fig. 7. Horizontal Trail.

Fig. 8. Horizontal Trail.

Fig. 9. Horizontal Trail.

Fig. 10. Vertical Trail.

Fig. 11. Vertical Trail.

Fig. 12. Vertical Trail.

Fig. 13. Vertical Trail.

Fig. 14. Vertical Trail.

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APPENDIX

A. LSTM horizontal correlation score

Run1 Run2 Run3 Run4 Run5subject1 0.5076636571 0.645124959 0.6116656257 0.5872845326 0.6960302475subject2 0.0850776853 0.2638801952 -0.1131819614 -0.3842723961 -0.1433992222subject3 0.3457215525 0.2148575995 0.0789596486 0.2020955094 0.4020765944subject4 0.4966689696 0.5279929687 0.4560842338 0.4905298773 0.5082408728subject5 0.4628946834 0.18884032 0.7157308532 0.1566183961 0.2727192139subject6 0.6652057832 0.7118272393 0.7336787637 0.6921853677 0.8425432451subject7 0.7545572677 0.644781832 0.7856362412 0.7932139533 0.8158821094subject8 0.6008635433 0.5620596684 0.6747443003 0.7296894759 0.6369650702subject9 0.5843552783 0.5825507128 0.6294251711 0.4726119053 0.7585664011subject10 0.3524809397 0.1987191066 0.1743496867 0.3579235199 0.4977369546subject11 0.1389092961 0.0023499277 0.1513526848 0.5617492995 0.6206556916subject12 0.411885007 0.4512045504 0.4815249995 0.5003187305 0.7075866502subject13 0.6636678781 0.6945207826 0.7439890984 0.7045944076 0.6738797934subject14 0.5721110223 0.620938929 0.5266556103 0.6422987624 0.5803448511subject15 0.2417116665 -0.0415361564 0.2031259604 0.0309347934 0.2364860736subject16 0.0309863512 -0.0717127042 -0.0081039464 -0.0329112779 -0.1108109082subject17 0.4558189244 0.7180231837 0.6903473436 0.7286291607 0.7621386576subject18 0.0222037391 -0.0812381311 -0.1121655841 0.2700371845 -0.1072683562subject19 0.4622096758 0.4125649527 0.4828217901 0.2307011487 0.0460894904subject20 -0.0173908724 -0.0549314235 -0.0475846513 0.05991706 0.0274713995subject21 0.4797889043 0.5508038782 0.5906760184 0.7340431858 0.8244500178subject22 0.562378563 0.5957195177 0.5692890987 0.7812170175 0.4691082606subject23 0.117878572 -0.0586053159 -0.1314836141 0.4855793917 0.4839875239subject24 0.2300566634 0.0210997209 -0.1570637943 -0.0517792954 0.0430626341subject25 0.8045437472 0.790070722 0.7127607229 0.7986044182 0.7401999788subject26 -0.1266298337 -0.0247937628 0.0107008245 -0.1186539977 -0.0262573019subject27 0.4936070446 0.318586627 0.5460875636 0.7130727263 0.5371527851subject28 0.787646465 0.4865097438 0.713819184 0.6788249743 0.6346528808subject29 0.1381964445 0.3745356354 0.6901720667 0.0840266655 0.4601193743subject30 0.4174411296 0.4306454237 0.6593857748 0.7442358206 0.5748201783subject31 0.6529631209 0.7052536247 0.676560907 0.7342609379 0.7316658949subject32 0.4702769282 0.0943070142 0.5526138529 0.6965209094 0.714604135subject33 0.5885428693 0.55842894 0.5246484414 0.0096614017 0.2280558612subject34 0.6011402523 0.3716490748 0.2543952879 0.4304882914 0.6105689058

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B. LSTM vertical correlation scoreRun1 Run2 Run3 Run4 Run5

subject1 0.185050739 -0.0423576095 0.1113440563 0.0596364906 0.0265220272subject2 -0.0863451387 0.044693438 0.1796289312 -0.1504774487 0.2338008484subject3 0.4088331427 0.393519206 0.3551285224 0.3872550087 0.2827671204subject4 0.0824757024 0.1063781346 0.2466644981 0.1879446834 0.300510718subject5 0.1111708515 0.1937771604 0.1943775771 0.4156019165 -0.0388266505subject6 0.3115957264 0.2272856706 0.3536592796 0.4009670463 0.3466741084subject7 0.4585675531 0.5166373091 0.6920807796 0.5907701149 0.3568205917subject8 0.0237069496 0.0245300172 0.026462015 0.1099605446 0.1368275341subject9 0.4199951377 0.3427221499 0.2681371307 0.2196650653 0.3946415977subject10 -0.1598008076 0.0797142082 0.1806072658 0.130381022 0.0862414385subject11 0.0428671552 0.1553074793 0.0819627183 -0.0312569765 0.0037622199subject12 0.130297919 0.1085120827 0.1613901366 0.2775434887 0.0173746515subject13 0.6246429692 0.3867363855 0.40891341 0.107626702 0.2010661844subject14 0.5036875796 0.6371898931 0.6401385778 0.6037418207 0.6726079933subject15 0.3716616049 0.3561925674 0.3896143567 0.0788610907 0.1276952933subject16 0.2185246771 -0.1131208236 -0.0773545913 -0.0416004427 -0.2473734546subject17 0.2177417661 0.2916970285 0.3585326942 0.4291216628 0.3397690191subject18 0.1857663585 0.1576383343 -0.0371485101 -0.0288131736 -0.1512133022subject19 0.7062645218 0.4105900393 0.4305046905 0.529866381 0.5426950421subject20 0.2486869601 0.0895215613 0.2530959192 0.1528296816 -0.12112433subject21 -0.0878669103 -0.2562990323 0.1619594985 0.2008665238 0.3014164986subject22 0.4510667196 0.1618149419 0.5272946273 0.4826815914 0.6497938082subject23 -0.0200612279 0.1212714021 0.3044104092 0.2099038327 0.085689642subject24 0.0458348111 -0.0585906423 0.0952996692 0.1262430811 -0.0009647752subject25 0.7694860247 0.4768628811 0.4501343629 0.5432072253 0.5878761954subject26 -0.0382264811 0.1504996037 -0.0821203787 0.0038175541 0.2614304408subject27 -0.1844682363 -0.0469997253 -0.1632362017 -0.1063765548 -0.0584438836subject28 0.4358649667 0.5289998119 0.4461715871 0.5098944446 0.5709473657subject29 0.1534396832 -0.0284600309 0.0014763702 0.0774240666 0.1460639742subject30 0.4052648503 0.1554749486 0.2102493942 0.2141553758 0.1503049224subject31 0.3520593518 0.5013209307 0.1961242021 0.2788054555 0.4850074799subject32 0.1764147525 0.1669522396 0.0139248417 0.1683672475 0.0887244161subject33 0.3177852063 0.0834442453 0.1852587057 0.5245059276 0.4442235066subject34 0.3723913703 0.2581854026 0.4149817439 0.2341260055 0.3449301811

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C. GRU horizontal correlation scoreRun1 Run2 Run3 Run4 Run5

subject1 0.507391356 0.5232378091 0.4818075791 0.485866737 0.5703828058subject2 0.2019135369 0.0358266314 -0.0003294678 0.1075474333 -0.0500747051subject3 0.5226517244 0.3397528427 -0.0498980455 0.2203027422 0.3940553666subject4 0.4981298753 0.4239849966 0.4111961314 0.5268868044 0.3890185338subject5 0.1444571372 0.3575640791 0.7356762939 0.7343000225 0.6813069396subject6 0.6123423189 0.6529514513 0.6348723686 0.587304807 0.8203324452subject7 0.7376194823 0.6296476286 0.7302419799 0.7112857688 0.7208293969subject8 0.6147971719 0.1972201513 0.4375670388 0.5448098333 0.5627839772subject9 0.5583268931 0.5054590359 0.3341011624 0.5954658891 0.6949345941subject10 0.3951585601 0.1241117323 0.0239358535 0.3555851007 0.5139293549subject11 0.1518876197 0.06759866 0.171978676 0.6649664417 0.6688354169subject12 0.3586129263 0.3846482077 0.4131543945 0.636247161 0.4258266983subject13 0.604467859 0.6331841233 0.5838752463 0.6893981491 0.5410379934subject14 0.5554180081 0.5421821042 0.5248998047 0.5514262441 0.5615870656subject15 0.0313179567 0.2367488928 0.2225547885 0.2868798821 0.2239870132subject16 -0.0207235439 0.0329072081 -0.049191833 0.0600621611 -0.0062364303subject17 0.3747606356 0.3773234314 0.504406738 0.5978477471 0.587004555subject18 -0.0956501455 0.1900986892 0.0485279657 0.0033586644 0.0070002171subject19 0.4904619596 0.3750804291 0.4004481226 0.2327642551 0.0664008498subject20 0.0866086095 -0.1971877602 -0.1484117455 0.0883586305 -0.1962944772subject21 0.362560825 0.400979408 0.5547384209 0.7191861211 0.7951705693subject22 0.4754322336 0.4439481596 0.1579305346 0.316351537 0.2466372936subject23 0.2092570865 -0.0275378902 -0.0616035012 0.4057651641 0.4581361496subject24 0.1071057885 -0.0483035856 0.0962883308 0.1121542634 0.0900318971subject25 0.785804105 0.6729152913 0.6799721725 0.6844032586 0.6346622363subject26 -0.1094422439 0.1899442674 -0.0916050884 0.043957218 -0.0202110952subject27 0.5039326716 0.3171296117 0.4361049457 0.43221237 0.4593290395subject28 0.7619902289 0.4767539206 0.5957916612 0.6431060485 0.5709828159subject29 0.1495123879 0.4250947796 0.6203196022 0.1103539952 0.4755142742subject30 0.4091685156 0.3851552618 0.7081728223 0.5210361237 0.4955741047subject31 0.64442666 0.6694025668 0.6674048249 0.6439668646 0.5733883142subject32 0.4336991025 0.1332288126 0.3413180773 0.4141424566 0.5647665771subject33 0.6014924438 0.5867436553 0.4787350252 0.0855120643 0.3169837098subject34 0.5787868776 0.2995843856 0.2659302962 0.5286368189 0.5697197027

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D. GRU vertical correlation scoreRun1 Run2 Run3 Run4 Run5

subject1 0.0209582753 -0.145266789 0.1305428996 0.1903079988 -0.0456048203subject2 0.0838462921 0.1353204841 0.1344900053 0.0650535649 0.1771306301subject3 -0.1403130935 0.0870847586 -0.015262579 0.2751617716 0.1949031212subject4 0.1152300588 0.1469303118 0.0245557149 0.0824120583 -0.0076982485subject5 -0.1541448345 0.2088806167 0.1823341521 0.321332852 0.1208217411subject6 0.1604930284 0.1815610425 0.267699332 0.3758618604 0.2979646291subject7 0.3183940581 0.504629309 0.709357792 0.4691812215 0.2521183967subject8 0.014903205 -0.0515517293 0.1243612078 0.3446488036 0.2444922725subject9 0.6026259523 0.2969692182 0.3061244401 0.3865776287 0.2111049301subject10 -0.4256968783 -0.0112827522 -0.031549324 0.1196699371 0.0452912214subject11 0.1327218911 0.2194082447 -0.0224768022 -0.0315867993 0.0463254787subject12 0.1160439464 -0.1039278814 0.0391307068 0.0128617335 0.1477110039subject13 0.6399900448 0.0712109663 0.4574700513 0.3005867775 0.6499938364subject14 0.4897551086 0.7112638132 0.6672966732 0.5597494966 0.6345984593subject15 0.3772969663 0.5354009913 0.201221078 0.1770625961 0.1558265721subject16 0.1043164235 0.131562045 0.1017218282 -0.0104754259 -0.1684331384subject17 0.1485410254 -0.0785396069 0.313524504 0.3409762911 0.3104112574subject18 0.1770781305 -0.14396933 -0.0608705042 0.0008775794 -0.081566931subject19 0.6892464159 0.4126285003 0.4441977871 0.6033473949 0.4470875452subject20 0.1303225336 -0.1874740859 -0.1557538898 0.2306973026 0.2315594139subject21 -0.1428795739 -0.2724132523 0.200719676 0.3733784136 0.3636131123subject22 0.4545995495 0.0682384275 0.2575905165 0.70428685 0.6306396182subject23 -0.0155044857 -0.044910378 -0.0475390821 -0.0059164813 -0.1256309008subject24 -0.0879396397 -0.1051895608 0.2000384114 -0.0094486301 -0.0337369882subject25 0.7028740112 0.5015865978 0.5153302787 0.5702842495 0.5688033339subject26 -0.0077243826 0.082355598 -0.1799721768 -0.2451539572 0.2046388953subject27 -0.2117295494 0.091729284 0.2084582075 0.1191121102 0.2395547612subject28 0.4420268818 0.5111619062 0.431031373 0.4882208 0.5789438365subject29 -0.1539439707 0.0321514259 0.0727362774 0.1321036033 0.1371992034subject30 0.4030181486 0.2979496417 0.228511181 0.174678393 -0.0226221739subject31 0.3608799192 0.4007776753 0.2114075656 0.222582294 0.3845956552subject32 0.0092243985 0.0932064796 0.0653939882 -0.1707916539 0.007241878subject33 0.1437651822 -0.1801064393 0.2168620995 0.1488250035 0.2573596365subject34 0.3442759368 0.2627647224 0.388484011 0.2781417424 0.3240536305

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ACKNOWLEDGMENT

This project is sponsored by the Na-tional Science Foundation through Re-search Experience for Undergraduates(REU) award, with additional supportfrom the Joint Institute of Computa-tional Sciences at University of TennesseeKnoxville. This project used allocationsfrom the Extreme Science and EngineeringDiscovery Environment (XSEDE), whichis supported by the National Science Foun-dation. In addition, the computing workwas also performed on technical worksta-tions donated by the BP High PerformanceComputing Team.

Special thanks to the mentors: Dr. Xi-aopeng Zhao, Dr. Kwai Wong and SoheilBorhani.

REFERENCES

[1] Van Gerven, M., Farquhar, J., Schaefer, R., Vlek,R., Geuze, J., Nijholt, A., Ramsey, N., Haselager,P., Vuurpijl, L., Gielen, S. And Desain, P. 2009.The brain-computer interface cycle. Journal ofNeural Engineering. 6, 1-10

[2] J. van Erp, F. Lotte, M. Tangermann Brain-computer interfaces: beyond medical applicationsComputer, 45 (4) (2012), pp. 26-34

[3] K.-R. MULLER, A. Kubler Toward brain computerinterfacing Massachusetts Institute of Technology(2007), pp. 1-25

[4] Abdulkader, S., Atia, A. and Mostafa, M. (2018).Brain computer interfacing: Applications and chal-lenges.

[5] D.S. Tan, A. Nijholt, Brain-computer interfaces:applying our minds to human-computer interaction

[6] Niedermeyer E.; da Silva F.L. (2004). Electroen-cephalography: Basic Principles, Clinical Applica-tions, and Related Fields. Lippincott Williams &Wilkins.

[7] Intro to Brain Computer Interface. NeurotechEDU,learn.neurotechedu.com/introtobci/

[8] Morash, V., et al., Classifying EEG signals pre-ceding right hand, left hand, tongue, and right footmovements and motor imageries. Clinical neuro-physiology, 2008. 119(11): p. 2570-2578.

[9] Wolpaw, J.R., et al., An EEG-based brain-computerinterface for cursor control. Electroencephalogra-phy and Clinical Neurophysiology, 1991. 78(3): p.252-259.

[10] Wolpaw, J.R. and D.J. McFarland, Control of atwo-dimensional movement signal by a noninva-sive brain-computer interface in humans. Proceed-ings of the National Academy of Sciences of theUnited States of America, 2004. 101(51): p. 17849-17854.

[11] Xia, B., et al., A combi- nation strategy based braincomputer interface for two-dimensional movementcontrol. Journal of neural engineering, 2015. 12(4):p. 046021

[12] McFarland, D.J., W.A. Sarnacki, and J.R. Wolpaw,Electroencephalographic (EEG) control of three-dimensional movement. J Neural Eng, 2010. 7(3):p. 036007

[13] Ernst Niedermeyer, Fernando Lopes da Silva Elec-troencephalography. Basic principles, Clinical Ap-plications and Related Fields. 3rd edition, Williams& Wilkins Baltimore 1993

[14] Trejo, L.J., R. Rosipal, and B. Matthews, Brain-computer interfaces for 1-D and 2-D cursor con-trol: designs using volitional control of the EEGspectrum or steady-state visual evoked potentials.Neural Systems and Rehabilitation Engineering,IEEE Transactions on, 2006. 14(2): p. 225-229

[15] A hybrid ERD/SSVEP BCI for continuous simul-taneous two dimensional cursor control. Journal ofneuroscience methods, 2012. 209(2): p. 299-307.

[16] Li, Y., et al., An EEG-based BCI system for 2-D cursor control by combining Mu/Beta rhythmand P300 potential. Biomedical Engineering, IEEETransactions on, 2010. 57(10): p. 2495-2505.

[17] Kim, S.-P., et al., Neural control of computer cursorvelocity by decoding motor cortical spiking activ-ity in humans with tetraplegia. Journal of neuralengineering, 2008. 5(4): p. 455

[18] Wikipedia contributors. (2018, June 12).Butterworth filter. In Wikipedia, The FreeEncyclopedia. Retrieved 17:24, July 27, 2018,from https://en.wikipedia.org/w/index.php?title=Butterworth_filter&oldid=845468206

[19] Jinhua Zhang, Jiongjian Wei, Baozeng Wang, JunHong, and Jing Wang, Nonlinear EEG DecodingBased on a Particle Filter Model, BioMed ResearchInternational, vol. 2014, Article ID 159486, 13pages, 2014.

[20] Wikipedia contributors. (2018, July 25).Recurrent neural network. In Wikipedia, TheFree Encyclopedia. Retrieved 14:34, July 30,2018, from https://en.wikipedia.org/w/index.php?title=Recurrent_neural_network&oldid=851966165

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