DETECTION AND CORRECTION OF EYE BLINK ARTIFACT IN SINGLE CHANNEL ELECTROENCEPHALOGRAM (EEG) SIGNAL USING A SIMPLE k-means CLUSTERING ALGORITHM

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1 Volume 120 No , ISSN: (on-line version) url: DETECTION AND CORRECTION OF EYE BLINK ARTIFACT IN SINGLE CHANNEL ELECTROENCEPHALOGRAM (EEG) SIGNAL USING A SIMPLE k-means CLUSTERING ALGORITHM Ajay Kumar Maddirala 1, Bhagya Raju Pullagura 1 1 Department of Electronics and Communication Engineering Narasaraopeta Engineering College (Autonomous), Narasaraopet. Corresponding Author: ajaymaddirala@gmail.com June 26, 2018 Abstract Electroencephalogram (EEG) Signals represent the electrical activity of the brain. These signals were used in diagnosing the brain disorders such as epilepsy and sleep disorders. However, these signals often contaminated by eye blink artifact also called electrooculogram (EOG) signals. In general, these artifacts appear in the recorded EEG signals due to the eye blink activity and cannot be controlled by the subject, as the eye blink is an inevitable activity. In this paper, we are proposing a novel technique to detect and correct the eye blink artifacts in the EEG signals based on k-means clustering algorithm and a low pass-filter. The results show that the proposed technique is able to detect and correct the blink artifacts from single channel EEG signals

2 1 INTRODUCTION Electroencephalogram (EEG) signals represents the electrical activity of the brain and used to diagnose brain disorders, such as epilepsy and sleep disorder. Moreover, the EEG signals are used in the brain computer interface (BCI) application, where the features of brain signals were used in deriving a command signal to control the device. Recently, the portable EEG devices are widely used to record the brain signals, both in clinical and home environment. These devices facilitates more comfort to the subject under test and also provides long time recording facility. In general, while recording, the EEG signals contaminated by several artifacts. Among which, an eye blink artifact is a predominant component frequently present in the recorded EEG signal, as the eye blink is an inevitable activity. Usually, these artifacts are more predominant in frontal EEG channels (Fp1 and Fp2). Several studies revealed that the presence of these artifact obscure the desired EEG components. Hence, detection and correction of these artifact have to be carried out to further analysis of EEG signals. Independent component analysis (ICA) is a blind source separation (BSS) technique often used to remove the eye blink artifact from the multichannel EEG data [1], [2]. In all these methods, the user has to manually identify the eye blink component to remove these artifacts from the multichannel EEG data. Recently, several methods have been proposed to remove the eye blink component automatically using ICA [3][8]. Since the portable EEG devices comprise single/few EEG channels to record the brain signals, tradition BSS technique cannot be used to remove the eye blink artifact from single channel EEG signal. The application ICA to single channel EEG signal is first proposed in [9]. However, this method will work based on the two constraints: (i) sources should be disjoint in their frequency spectrum and (ii) sources should be stationary signals. As the EEG signal is non-stationary signal, these condition cannot be hold. Recently, in [10], [11], ICA technique is combined with decomposition techniques (discrete wavelet transform (DWT) and ensemble empirical mode decomposition (EEMD) [12]) to process the single channel EEG signals. In these method, first, the single channel EEG signal is decomposed into multivariate data and

3 then ICA is applied this data to extract the components. However, as it extracts the components based on the higher order statistics (HOS), ICA involves more computations. Moreover, faithful separation of components is still left as a challenging problem. Singular spectrum analysis is a subspace based decomposition technique often used to extract the trends, oscillating and noise components from climate time series data [13], [14]. The application of SSA to biomedical signal is first proposed in [15]. Later, it has been widely used to process the single channel EEG signals [16][19]. As it uses the second order statistics (SOS) of the data, faithful separation of artifact components is not possible. To address this problem, in this paper, we proposed simple and efficient technique to detect and correct the eye blink artifact from single channel EEG signals. To detect the eye blink components, first, the single channel EEG signal is mapped into multivariate data. Secondly, the features of each column vector in the multivariate data is computed and then k-means clustering algorithm is applied. Based on the clustering information, we extracted the eye blink component and detected their regions in the component. Finally, the extracted eye blink component is filter and subtracted from the contaminated EEG signal to obtain the corrected EEG signal. 2 PROPOSED METHOD FOR EYE BLINK DETECTION Consider the single channel contaminated EEG signal as x = s + r, where s and r represents the true EEG and eye blink components respectively. To detect the eye blink activity in the EEG signal, first the M sampled signal vector x = [x(1), x(2),..., x(m)] is mapped into multivariate data X of size N K and given as where, N is the window length and K = M - N + 1. Since the contaminated signal x is mixer of two components s and r, then

4 the trajectory matrix X = S + R, where, S and R are representing the trajectory matrices of true EEG and eye blink components respectively. Next, energy and local mobility [20] (fluctuation index) features of each column vector in (1) are computed. Once after computing the features of each column vector in (1), a k-means clustering algorithm [21] is applied on them with the number of clusters set to two. Based on the clustering information two trajectory matrices Ŝ and ˆR of size N K were constructed. Here, the matrices Ŝ and ˆR are representing the estimated true EEG and artifact components respectively. The univariate true EEG ŝ and eye blink ˆr components were reconstructed from these trajectory matrices using diagonal averaging step. Consider r(i,j) an element of a trajectory matrix ˆR, where i = {1,2,3,...,N} and j = {1,2,3,...,K}. Then the diagonal averaging operation on matrix {1,2,3,...,N} can be computed using the following equations. To detect the eye blink activity in an EEG signal, first, eye blink component should be identified from the reconstructed components obtained by the diagonal averaging procedure. Here, the correlation coefficient (CC) of each component is computed to identify the eye blink component. Since the eye blink is a low frequency component, we expect its corresponding CC value is high. Next, the squaring operation is performed on the eye blink component. Finally, eye blink detector output d(n) is derived as follows The extracted eye blink artifact is passed thorough a low-pass filter with a cut-off frequency 8Hz to remove the high frequency components. Finally, the corrected EEG signal is obtained by subtracting the filter output from the contaminated EEG signal

5 3 RESULTS AND DISCUSSION To evaluate the performance in detecting eye blinks, we have considered the EEG data recorded for brain computer interface [22] and maore details about the EEG data is available in [22]. We have downloaded the EEG data from PhysioBank [23]. The EEG signals were recorded at a sampling frequency f s = 2048Hz. However, in order to reduce the computational burden, we have down sampled the signal to f s = 256Hz. The raw EEG signal is filter using a high-pass filter (f c = 0:5Hz) to remove the low frequency components (baseline drift). First, the given single channel EEG signal is mapped into multivariate data matrix X of size N M. In our simulations, we set N value to 0:5fs, as the eye blink activity last for 0:5s. We computed the energy and the local mobility features of each column vector in X. Fig. 1 shows the EEG signal and the extracted features of K column vectors of X. We have extracted the energy and the local mobility features of each column vector. The reason for choosing these features is that the energy and the local mobility features are distinct from the eye blink to non eye blink regions. It is evident from Fig. 1(b) & (c) that the energy and local mobility of column vectors associated to the eye blink are having high and low values respectively. As the eye blink activity results a signal of low frequency with high amplitude, therefore the local mobility (fluctuation index) and the energy are low and high respectively. Fig. 1 (d) shows the scatter plot of feature points (x-axis: energy, y-axis: local mobility). It is identified that the feature points associated to eye blink vectors (red) are distinct from the feature points associated to the non eye blink vectors (blue). We have applied k-means clustering algorithm on the extracted features to cluster the features (which means that indirectly on the column vectors of matrix X) associated to eye blink and non eye blink regions. Since we have assumed that the signal is mixture of two components, here, the number of clusters is set to two. Based on the clustering information we have constructed two matrices called Ŝ and ˆR. Which means that based on the clustering information, we have identified the indices of column vectors of X corresponding to non eye blink (cluster 1) and eye blink (cluster 2), and

6 Figure 1: (a) Eye blink contaminated EEG signal recorded at Fp1 (first K samples), (b) & (c) the energy and the local mobility features of column vector of X respectively and (d) scatter plot of feature points

7 Figure 2: (a) Eye blink contaminated EEG signal, (b) extracted EEG component and (c) the extracted eye blink component using proposed method

8 constructed two matrices of size N M. Note that we have initialized all the elements of two matrices to zeros. Once after identifying the indices of vectors, they were placed in the corresponding column of the matrix, let us say ˆR (cluster 2). For example, if the vector index is two, then we place the vector in the second column of the matrix ˆR. In this way two matrices, one representing true EEG signal and other representing eye blink component. Finally, the diagonal averaging step is applied on two matrices and extracted EEG and eye blink components. Fig. 2 show the extracted EEG and eye blink components from the contaminated single channel EEG signal. It is clear from Fig. 2 (b) & (c) that both EEG and eye blink components are efficiently extracted from the contaminated EEG signal in Fig. 2(a). Finally, in order to detect the eye blink regions in the given EEG signa, first, the eye blink component from the extracted two components should be identified. As the eye blink component is a low frequency signal, the CC value is high (maximum one). Based on the CC value, we have identified the eye blink component and applied squaring operation on it. Finally, the detector output is set as one if the squared value is greater than zero and is set to zero other wise. Fig. 3 shows the eye blink detector output using k-means clustering algorithm. It is evident from Fig. 3 (d) that the proposed method efficiently identified the eye blink regions in the contaminated EEG signal. In order to obtain the corrected EEG signal, first, the high frequency components superimposed on the eye blink artifact component (Fig. 3(b)) are removed using low-pass filter. The filtered eye blink components, shown in Fig. 4(b) is subtracted from the contaminated EEG signal (Fig. 4(a)). It is clear from Fig. 4(c) that the proposed method efficiently removed the eye blink artifacts from the contaminated EEG signal. 4 CONCLUSION In this paper, we have proposed a method to detect and correct the eye blink artifacts from single channel EEG signals. In this method, first, simple k-means clustering algorithm is employed to detect the eye blink activity in single channel EEG signal. Next,

9 Figure 3: (a) Eye blink contaminated EEG signal, (b) extracted eye blink component, (c) square of eye blink component and (d) superposition of detector output (red color) on the contaminated EEG signal

10 Figure 4: (a) Eye blink contaminated EEG signal, (b) filtered eye blink component, (c) corrected EEG signal

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