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

Abstract

Electroencephalogram (EEG) is a diagnostic test, and it measures the entire brain's 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, δ rhythm, θ rhythm, α rhythm, β rhythm and y 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 δ rhythm to obtain both LP and TV components. The filtered δ rhythm sub-signal is obtained based on the subtraction of both LP and TV components from the contaminated δ rhythm sub-signal. The filtered EEG signal is evaluated by combining the filtered δ rhythm with θ rhythm, α rhythm, β rhythm, and y rhythm subsignals. The energy ratio of the δ 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 α rhythm as compared to other existing techniques.