Power Line Interference Cancellation from EEG Signals using RLS Algorithm

This paper investigates a new approach employing recursive least square (RLS) adaptive algorithm for cancellation of power line interference (PLI) from Electroencephalogram (EEG) signal. The EEG signal is taken from the standard MIT-BIH Polysomnographic database. The proposed RLS algorithm based noise canceller is compared with least mean square (LMS) and normalized LMS (NLMS) algorithm based noise canceller. The results illustrate that adaptive algorithms can efficiently estimate and reject the noise in acquired EEG signals however; RLS algorithm gives better performance as compared to LMS and NLMS algorithm. Thus, the proposed noise canceller enhances the reliability of estimated EEG signal which can subsequently be utilized for establishing Brain Computer Interface.