6.2 Simulation Studies
6.2.1 Muscle Artifact Removal from Synthetic EEG Signals
Before applying the artifact removal technique on synthetic EEG signals, we set the free parameters of proposed SSA and other two decomposition techniques as follows. For DWT decomposition technique, Daubechie 10 (db10) mother wavelet and 5 decomposi- tion levels were used. The free parameters of EEMD technique, i.e. npand ne were set to2and100, respectively. Finally, the window lengthM of the proposed SSA technique is selected based on the criteria i.e. M > fs/f [69]. We considered fs = 250Hz and f = 5.5Hz and M = 64. As most of the energy in the ictal EEG signal is concentrated in between0.5Hz−16Hz band, Th for EEMD and the proposed SSA techniques is set to 16Hz.
RRMSE curves of the three decomposition techniques as a function of SNR is shown in Fig. 6.2. Here, we evaluate the performance of DWT decomposition technique usingdb4, db6,db8anddb10wavelet functions. It is obvious from Fig. 6.2 that DWT decomposition technique using db10 mother wavelet function showed better performance than DWT with other wavelet functions. However, it is observed from RRMSE curves that the performance of DWT mainly depends on the mother wavelet function. It is clear from Fig. 6.2 that EEMD exhibits poor performance as compared with than DWT (db10) and
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Figure 6.5: Reconstructed EEG signal forSN R= 1using (a) DWT, (b) EEMD and (c) proposed SSA techniques respectively.
better performance compared to the DWT (db4anddb6) for SNRs greater than one. The reason is that noise components, added in the decomposition process were not canceled properly in the process of average of IMFs. To suppress these noise components, either ne should be increased or np should be decreased. In [66], it is stated that np value should be large when the signal to be extracted is low frequency component and should be small when the signal to be extracted is high frequency component. However, the computational complexity of EEMD algorithm is increased whenne is increased. Since SSA accounts the local covariance of the data the estimated basis vectors (eigenvectors) are robust. Therefore, the proposed SSA technique outperforms both DWT and EEMD techniques. Moreover, unlike DWT, the selection of the parameterM in SSA is straight forward and is independent of the signal morphology. To compare the performance of proposed SSA and other two decomposition techniques in a subjective way, synthetically contaminated EEG signal of SN R = 1is used as a input signal. The decomposed sub- band signals of contaminated EEG signal using DWT are shown in Fig. 6.3. As the most of energy of ictal EEG signal is concentrated between0.5−16Hzband, sub-band signals obtained by detailed coefficients at decomposition levels 4 and 5 and approximation coefficient at level 5 are used for the reconstruction of artifact free EEG signal. The sub-band signals and the corrected EEG signal using DWT are shown in Fig. 6.3 and Fig. 6.5(a) respectively. The IMF components of the contaminated EEG using EEMD
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Figure 6.6: Zoomed version Fig. 6.5 between the time interval 1.5−2s: (a) DWT, (b) EEMD and proposed SSA decomposition techniques, respectively.
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Figure 6.7: The dominant frequencies (fd) of estimated eigenvectors ofuforSN R= 1.
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Figure 6.8: Dominant frequencies of M eigenvectors.
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Figure 6.9: (a) Contaminated EEG signal, The corrected EEG signal using (b) DWT, (c) EEMD and (d) proposed SSA technique.
are shown in Fig. 6.4. In order to reconstruct the artifact free EEG signal, first, PSD of each IMF is computed. Later, the dominant frequencyfd of each IMF is identified. The artifact free EEG signal is reconstructed by adding IMF components whose dominant frequency is less or equal to specified thresholdTh,i.e.16Hz. The corrected EEG signal obtained by EEMD is shown in Fig. 6.5(b). In the proposed technique after deriving
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Figure 6.10: PSD of (a) contaminated EEG signal, corrected EEG signals using (b) DWT, (c) EEMD, and (d) proposed SSA decomposition techniques, respectively.
the eigenvectors of the covariance matrix, the dominant frequency of each eigenvector is computed, shown in Fig. 6.7. The dotted line in red color shows the threshold Th, which is set to 16Hz. In order to obtain the corrected EEG signal using proposed SSA decomposition technique, first, trajectory matrix associated to artifact free EEG signals is obtained by projecting trajectory matrix Uonto the subspace spanned by the eigenvectors whose dominant frequencies are less than 16 (v1 to v4, v11,v22 and v45).
Finally, the trajectory matrix associated to the artifact EEG signal is mapped into single channel EEG signal using (2.15). The corrected EEG signal estimated by the proposed SSA technique is shown in Fig. 6.5(c). To show the similarity between the true EEG and the corrected EEG signals, a zoomed version of Fig. 6.5(a-c) is showed with ground truth EEG signal (blue color) in Fig. 6.6. The correlation coefficient between the ground true and corrected EEG signals is computed. From the computed correlation coefficients, it shows that corrected EEG obtained by the proposed SSA technique is more similar with the true EEG signal.