Abstract
EEG signals are among the weakest and most disturbing vital signals because with the slightest change in body posture, and various artifacts will be added to them. The presence of artifacts in the EEG signal leads to an incorrect analysis of this signal. Due to the importance of the subject, various methods have been proposed to eliminate these artifacts. In this thesis, the Wavelet-RLS modified method for removing eyelid articulation from the EEG signal is improved. We then compare the performance of the modified Wavelet-RLS method with the SNR and MSE criteria with the RLS and regular Wavelet-RLS methods. In this method, first, the noise signal is analyzed by the wavelet. Then the coefficients in the frequency bands, including the blinking effect, are filtered by a recursive least squares (RLS). Finally, the clean signal is reconstructed with the inverse Wavelet transformation. The results show that the performance of the modified Wavelet-RLS method is better than the regular Wavelet-RLS and RLS methods in terms of MSE and SNR.
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