Ocular Artifact Elimination from EEG signals using RVFF-RLS Adaptive Algorithm

Abstract

Ocular Artifacts (OAs) have a significant impact on the performance of Electroencephalogram (EEG) activities in the frontal region because of its higher amplitude. In this paper, Robust Variable Forgetting Factor (RVFF) and Recursive Least Square (RLS) based RVFF-RLS algorithm is implemented for removal of OAs from the raw EEG signal. Reference signals such as horizontal electro-oculogram and vertical electro-oculogram are recorded and then processed through the finite impulse response filter, whose coefficients are adaptively updated using the RVFF-RLS algorithm. Thereafter, obtained signals are subsequently subtracted from the raw EEG signal to obtain an EEG signal, which is free from OAs. The performance of proposed technique is compared with conventional techniques such as numerical variable forgetting factor RLS, fixed step size normalized least mean squares, fixed forgetting factor- RLS. The proposed technique shows least mean square error under a dynamic environment.