Elimination of Ocular Artifacts From Single Channel EEG Signals Using FBSE-EWT Based Rhythms

Electroencephalogram (EEG) is a diagnostic test, and it measures the entire brains electrical activity. The EEG signals have been used in many applications such as the diagnosis of neurological abnormalities, the brain-computer interface (BCI), the detection of sleep-related pathologies, etc. The EEG signal is contaminated with ocular artifact during the acquisition, and the filtering of this artifact is indeed required for efficient processing of this signal. In this work, we have proposed a method for the removal of ocular artifacts from the EEG signal. The Fourier-Bessel series expansion based empirical wavelet transform (FBSE-EWT) is used for the extraction of EEG rhythms namely, delta rhythm, theta rhythm, alpha $ rhythm, beta rhythm and gamma rhythm sub-signals from the ocular artifact contaminated EEG signal. The enhanced local polynomial (LP) approximation based total variation (TV) (LPATV) filtering is applied over the contaminated delta rhythm to obtain both LP and TV components. The filtered delta $ rhythm sub-signal is obtained based on the subtraction of both LP and TV components from the contaminated delta $ rhythm sub-signal. The filtered EEG signal is evaluated by combining the filtered delta $ rhythm with theta , alpha, beta, rhythm, and gamma rhythm sub-signals. The energy ratio of the delta rhythm and the mean absolute error (MAE) in the power spectral density (PSD) values for all other rhythms are used as the performance metrics for the evaluation of the proposed method. The experimental results reveal that the proposed method has a better performance with a minimum average MAE in PSD value of 0.029 for alpha rhythm as compared to other existing techniques.