Classification of Mental Task for Brain Computer Interface Using Artificial Neural Network

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1 Classification of Mental Task for Brain Computer Interface Using Artificial Neural Network Mohammad Naushad 1, Mohammad Waseem Khanooni 2, Nicky Ballani 3 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. DOI : /IJRTER XDMNO 145

2 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 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 All Rights Reserved 146

3 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 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 All Rights Reserved 147

4 Figure 3: Plot for PSD estimated using FFT Method Figure 4: Plot for PSD estimated using FFT Method with Kaiser Window 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 All Rights Reserved 148

5 xx N 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. 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 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, All Rights Reserved 149

6 Mean value of Welch estimate International Journal of Recent Trends in Engineering & Research (IJRTER) 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. Sr. No. Table1. Comparative results for real hand movement for B-Alert data No. of hidde n Neurons in each layers layer PSD Method Periodogram Periodogram Periodogram with Hamming 74 All Rights Reserved 150

7 Periodogram with Hamming window FFT method FFT method FFT method with Kaiser FFT method 66 with Kaiser Welch method Welch method 86 Table2. Comparative results for Imaginary hand movement for B-Alert data Sr. No. of hidden Neurons in No. layers each PSD Method layer Periodogram Periodogram Periodogram with Hamming window Periodogram with Hamming window FFT method FFT method FFT method with Kaiser FFT method with 63 Kaiser Welch method Welch method 62 Sr. No. Table3. Comparative results for Imaginary hand movement for BCI data No. of hidden layers Neurons in each layer PSD Method Periodogram Periodogram Periodogram with 74 Hamming window Periodogram with 72 Hamming All Rights Reserved 151

8 FFT method FFT method FFT method with Kaiser FFT method with Kaiser Welch method Welch method 66 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 1 Periodogram 62 2 Periodogram with Hamming window 74 3 FFT method 88 4 FFT method with Kaiser 92 5 Welch method Table5. Comparative results for Imaginary hand movement for B-Alert data Sr. No. PSD Method 1 Periodogram 50 2 Periodogram with Hamming window 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. with with Averag e PSD Method Trail1 Trail2 accurac y 1 Periodogram Periodogram with Hamming window 3 FFT method FFT method with Kaiser 5 Welch method All Rights Reserved 152

9 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. REFERENCES 1. Padmanabh Mahesh Lanke, Prof. R. K. Shastri, Prof. S. D. Biradar EEG Signal Processing Techniques for Mental Task Classification, International Journal of Advanced Computing and Electronics Technology (IJACET), ISSN(PRINT): , (ONLINE): ,VOLUME-2,ISSUE-1, Yang Zong-chang, Establishing Structure for Neural Networks Based-on Fractal. Journal of Theoretical and Applied Information Technology 10th March Vol. 49 No.1, ISSN: , E-ISSN: p. 342_ H. C. So, Y. T. Chan Comparison of Various Periodograms for Sinusoid Detection and Frequency Estimation, IEEE Transactions on Aerospace and Electronic system VOL. 35, NO. 3 pp JULY Soosan Beheshti Mean-Square Error in Periodogram Approaches with Adaptive Windowing IEEE Transaction on signal processing VOL. 59, NO. 3.pp , March Sem a Arslan, Gulay T ezel, and Ha kan Islk, EEG Signals Classification Using a Hybrid Structure of ANN and PSO, International Journal of Future Computer and Communication, Vol. 1, No. 2, August Hansa Rani Gupta, Rajesh Mehra, Power Spectrum Estimation using Welch Method for various Window Techniques, International Journal of Scientific Research Engineering & Technology (IJSRET) Volume 2 Issue 6 pp September 2013, ISSN Mythra H V, Veenakumari H M, Sanjeev Kubakaddi., Multi-Class EEG Classification for Brain Computer Interface, International Journal of Scientific & Engineering Research, Volume 4, Issue 9, September-2013 p Shaik.Jakeer Husain and K.Srinivasa.Rao Epileptic Seizures Classification from EEG Signals using Neural Networks, 2012 International Conference on Information and Network Technology (ICINT 2012) IPCSIT vol. 37 p (2012) (2012) IACSIT Press, All Rights Reserved 153