Classification of Mental Task for Brain Computer … · Classification of Mental Task for Brain...

DOI : 10.23883/IJRTER.2017.3058.XDMNO 145

Classification of Mental Task for Brain Computer Interface Using

Artificial Neural Network

Mohammad Naushad1, Mohammad Waseem Khanooni2, Nicky Ballani3 1,2,3 Department of Electronics and Telecommunication Engineering,S. B Jain Institute of Technology

Management & Research, Katol Road, Nagpur, India

Abstract: This paper deals with classification of mental task using PSD and ANN. In this work feature

is extracted by using different PSD methods such as periodogram method, Welch method etc. After

extracting the features signal is given to Neural Network for classification. In this work Back

Propagation Algorithm has been used to train the Neural Network. The two task left hand and right

hand real movement and left hand and right hand imaginary movement have been classified, and the

comparison between real and imaginary movement has been discussed. The EEG signal is recorded

using B-Alert system and also EEG data have been taken from BCI data base (BCI competition III

data set V).

Keywords: Power Spectral Density (PSD), Artificial Neural Network (ANN) Electroencephalogram

(EEG), Brain Computer Interface (BCI).

I. INTRODUCTION

A BCI is a computer-based system that acquires brain signals, analyzes them, and translates them into

commands that are relayed to an output device to carry out a desired action. In recent years mental

tasks have been studied by many researchers and their study shows that BCI is useful for physically

challenged individual. Such individuals who have lost their entire voluntary actions can only rely on

their cognitive actions to interact with others BCI is useful for them [1]. An electroencephalogram

(EEG) is the basic building block for Brain-Computer Interfaces. The Electroencephalogram (EEG) is

used to measure the brain signals pertaining to various activities like imagining hand movements leg

movement etc. The EEG recognition procedure mainly involves feature extraction from EEG signal

and classification of mental task. The useful EEG signals contain huge data of brain signals. Numerous

methods have been used to extract feature vectors from the EEG signals. The different PSD methods

have used to extract the feature. The Fourier presented his famous method for representing signals in

the frequency domain; life scientists have employed his transform in order to determine cyclical events

in nature. The PSD is a powerful method for the identification of repetitive and correlated events, and

was first refined by Schuster in the late 1800s, in order to accurately quantify natural phenomena for

lunar and sunspot-induced earthquake predictions. Known as the periodogram, the resulting PSD is

formed by averaging many Fourier spectra in order to improve signal to noise, and preserve frequency

resolution. Since that time, signal processing has blossomed in the field of communications and

biometric measurements. Non-parametric estimation methods involve simple averaging techniques of

multiple spectra, which is well within the capabilities of even modest computing equipment. In

addition to the basic periodogram, methods by Bartlett and Welch offer more advanced forms

involving pre-filtering, and data windowing. Recent approaches involve developing parameterized

models, whereby the PSD is represented by a high-order mathematical equation, instead of a simple

running average. The extracted feature vectors are then classified using machine learning techniques

like neural networks, linear discriminate analysis, support vector machines (SVM), common spatial

pattern, etc.

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 03, Issue 03; Month - 2017 [ISSN: 2455-1457]

@IJRTER-2017, All Rights Reserved 146

II. METHODOLOGY

The classification of mental task involves, EEG recording or EEG data from data base then the feature

extraction and finally classification.

2.1 Design Flow The EEG signals have been used for mental task classification, however the EEG signals are often

corrupted by power line interference noise and EMC induced noise. These artifacts strongly influence

the utility of recorded EEGs and need to be removed for better classification of mental task. Therefore

the signals were preprocessed and then used to extract the feature. The EEG signals are obtained from

B-Alert experimental setup for two subjects and also from BCI data base (BCI Competition III data

set V for three subjects). Then the different PSD methods have been employed for feature extraction.

The final step involves classification of mental task which has been done by using neural network

based on Back Propagation Algorithm.

Figure 1: Design Flow

2.2 Experimental paradigm

2.2.1 BCI Data set This dataset contains data from 3 normal subjects during 4 non-feedback sessions. The subjects sat in

a normal chair, relaxed arms resting on their legs. There are 3 tasks:

• Imagination of repetitive self-paced left hand movements, (left, class 2),

• Imagination of repetitive self-paced right hand movements, (right, class 3),

All 4 sessions of a given subject were acquired on the same day, each lasting 4 minutes with 5-10

minutes breaks in between them. The subject performed a given task for about 15 seconds and then

switched randomly to another task at the operator's request. EEG data is not splitted in trials since the

subjects are continuously performing any of the mental tasks. The algorithm should provide an output

every 0.5 seconds using the last second of data (see clarification in the paragraph 'Requirements and

Evaluation'.).

Data are provided in two ways:

• Raw EEG signals. Sampling rate was 512 Hz.

• Precomputed features. The raw EEG potentials were first spatially filtered by means of a

surface Laplacian. Then, every 62.5 ms --i.e., 16 times per second-- the power spectral density (PSD)

in the band 8-30 Hz was estimated over the last second of data with a frequency resolution of 2 Hz for

the 8 centro-parietal channels C3, Cz, C4, CP1, CP2, P3, Pz, and P4. As a result, an EEG sample is a

96dimensional vector (8 channels times 12 frequency components).



2.2.2 B-Alert Experimental Setup. The S.B. Jain Institute of Technology Management and Research has got grand from Research

Promotion Scheme (RPS) of AICET for BCI research work, under this scheme college has provided

us the two EEG recording system RMS EEG machine and B-Alert X10-Standard system. Here we

have discussed about B-Alert system.

B-Alert X10-Standard system is a wireless EEG data acquisition system. The data has been recorded

using B-Alert X10-Standard system. The experiment was done on two subjects. The subjects were

given to perform two different tasks left hand and right hand real and imaginary movement. For left

hand and right hand real movement data was recorded for around 3 minutes where as for left hand and

right hand imaginary movement recording was done for 2 minutes with sampling frequency of 256

Hz. X10-Standard system is a ten channels system the ten channels are ECG, F3, Fz, F4, C3, CZ, C4,

P3, POz and P4. In this 8 channels have been considered which are F3, Fz, F4, C3, CZ, C4, P3 and

POz.

2.3 Feature extraction: The EEG signals are used to classify the mental tasks; the feature is first extracted before classification.

The different PSD methods are used here to extract the features.

2.3.1 PSD Using FFT method: Power spectral density (PSD) is computed by performing Fast Fourier Transform (FFT) on a segment

of data that is of interest, and calculating the amplitudes of the sinusoidal components for designated

frequency bins. Input variables to this transformation are an EEG segment for which PSD is to be

computed, and its length; output variables include PSD amplitudes. Frequency domain variables are

based on the power spectral density derived after application of a 50% overlapping window, and a

FFT with and without application of a Kaiser window is calculated from 8Hz to 30 Hz for each EEG

channel that are logged to obtain a Gaussian distribution.

The 8 channels are (Poz, Fz, Cz, C3, C4, F3, F4, F3 and F3) used to calculate the PSD. The 12

components are calculated for each channel in 8Hz to 30 Hz band there by providing 96 components

for 8 channels. Both sets of PSD output files apply a 50% overlapping window which averages the

PSD across three x one-second overlays to smooth the data. The illustration below shows that overlays

0, 1, and 2 are averaged (each overlay containing 256 data points with 128 data points being shared

for each overlay) to provide the PSD values for epoch n:

Figure 2: Overlap Segment



Figure 3: Plot for PSD estimated using FFT Method

Figure 4: Plot for PSD estimated using FFT Method with Kaiser Window

2.3.2 Periodogram Method. The well-know known form of the PSD is called periodogram. It was originally introduced by Schuster

(1898) to detect and measure “hidden periodicities” in data. The periodogram is given as. [30]

The periodogram Pxx(f) can be computed by use of DFT, which in turn in efficiently computed by an

FFT algorithm. If we have N data points, we compute as a minimum the Npoint DFT. For example,

the computation yields samples of the periodogram

k= 0, 1, 2 …… N-1.

at the frequencies f = k/N

Samples of the periodogram at fk = k=N, k =0, 1, : : : ,N ¡1 can be computed efficiently with the use

of the fast Fourier transform (FFT) algorithm while values at the other frequencies are evaluated by

either zero padding or interpolation.



In this work Power spectral density (PSD) is computed by calculating periodogram without and with

hamming window for segment of data that is of interest. The periodogram is calculated till the last

epoch where each epoch consists of sequence of 256 samples.

Figure 5: Plot for PSD estimated using periodogram

2.3.3 Welch Method.

Power Spectral Estimate (PSE) is most important application area in Digital Signal Welch method

have two basic modifications to the Bartlett method. These are allowed the data length to overlap. The

data segment can be represented as

Where U is a normalization factor for the power

The Welch power spectrum estimate is the average of modified periodgram, is

Where iD is the starting point for the ith sequence. If D = M, the segment do not overlap and the L

of data sequence is identical to the data segment of Bartlett method. The second change in Welch

method is to window the data segments prior to computing the periodogram.

xx N



Mean value of Welch estimate

The resolution of estimated power estimation is determine by the spectral resolution of each segment

which is of length L. it is window dependent.

Welch allows the data segments to overlap and window the data segments prior to computing

preiodogram. Here the 256 point sequence is subdivided into 8 overlapping segments with 50%

overlap; each segment is windowed by hamming window. The again the PSD is calculated in same

frequency band 8 Hz to 30 Hz, 12 PSD components are calculated for each channel. Therefore for 8

channels 96 PSD components will be obtained.

Plot for PSD estimated using Welch Method

2.4 Classifier. In this work two task left hand and right hand real and imaginary movement has been classified using

Artificial Neural Network with different number of hidden layers and neurons. The back propagation

algorithm is used to train the neural network.

III. RESULT

In this work two task left hand and right hand real and imaginary movement has been classified using

Back propagation neural network with different number of hidden layers and neurons, where as the

feature is extracted using FFT method, Periodogram method, and Welch Method. The different result

i.e. classification accuracy has been obtained as shown in table below.

Table1. Comparative results for real hand movement for B-Alert data

Sr.

No.

No. of hidde n

layers

Neurons in each

layer

PSD Method

%

Accuracy

1 3 8 16 10 Periodogram 62

2 2 8 16 Periodogram 58

3 3 8 16 10 Periodogram with Hamming

window

74



4 2 16 10 Periodogram with Hamming

window

74

5 3 8 16 10 FFT method 92

6 2 8 10 FFT method 82

7 3 8 16 10 FFT method with Kaiser 88

8 2 8 16 FFT method

with Kaiser

66

9 3 8 16 10 Welch method 92.33

10 2 8 16 Welch method 86

Table2. Comparative results for Imaginary hand movement for B-Alert data

Sr.

No.

No. of hidden

layers

Neurons in

each

layer

PSD Method

%

Accuracy



3 3 8 16 10 Periodogram with Hamming

window

56.66

4 2 8 16 Periodogram with Hamming

window

46

5 3 8 16 10 FFT method 70

6 2 8 16 FFT method 50

7 3 8 16 10 FFT method with

Kaiser

70

8 2 8 16 FFT method with

Kaiser

63

9 3 8 16 10 Welch method 71


Table3. Comparative results for Imaginary hand movement for BCI data

Sr.

No.

No. of hidden

layers

Neurons in each

layer

PSD Method

%

Accuracy



3 3 8 16 10 Periodogram with

Hamming window

74

4 2 8 16 Periodogram with

Hamming window

72



5 3 8 16 10 FFT method 80

6 2 8 16 FFT method 73.33

7 3 8 16 10 FFT method with Kaiser 100

8 2 8 16 FFT method with Kaiser 100

9 3 8 16 10 Welch method 100


From above tables it can be seen that the accuracy of neural network increases as increase the number

of hidden layers but the complexity and computation time is also increased. The table below shows

the comparative results with different PSD techniques in term of maximum accuracy obtained.

Table4. Comparative results for real hand movement for

B-Alert data

Sr.

No.

PSD Method

%

Accuracy

1 Periodogram 62

2 Periodogram with Hamming window 74

3 FFT method 88

4 FFT method with Kaiser 92

5 Welch method 92.33

Table5. Comparative results for Imaginary hand movement for B-Alert data

Sr. No.

PSD Method

%

Accuracy

1 Periodogram 50

2 Periodogram with Hamming window 56.6

3 FFT method 70

4 FFT method with Kaiser 70

5 Welch method 71

For BCI competition III data set V, for subject 1 the two trials has been taken to calculate

classification accuracy the average classification accuracy is shown in table below.

Table6. Comparative results for Imaginary hand movement for BCI data base.

Sr. No.

PSD Method

%

Accuracy with

Trail1

%

Accuracy with

Trail2

%

Averag e

accurac y

1 Periodogram 50 50 50

2

Periodogram with

Hamming window 46 74 65

3 FFT method 60 80 70

4

FFT method

with Kaiser 100 100 100

5 Welch method 100 60 80



IV. CONCLUSION In this work the two task left hand and right real and imaginary movement have been classified for

which the EEG data has been recorded using B-Alert X10 standard wireless system on two subjects.

The comparative results for real hand movement for B-Alert data is given in table 4 and the

comparative results for imaginary hand movement for BAlert data is given in table 5. It is found that

the classification accuracy is found to be better in case of real hand movement.

The classification is also done for BCI competition III data set V, the classification accuracy for two

trials is shown in table 6. In which the accuracy with FFT method is found to be 100%.

The classification accuracy has also been calculated with different number of layers in neural network

and different number of neurons in each layer the as shown in table 1, table 2 and table 3. It can be

seen from table 1, table 2 and table 3 that the on an average the classification accuracy increases as we

increase the number of layer and neuron in the neural network.

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