Improving Capabilities of the Adaptive Recursive Least-Squares Filter in the Ocular Artifact Removal from EEG Signal

One of the most popular techniques in the Ocular Artifact (OA) removal from Electroencephalogram (EEG) signals is Adaptive Filter (AF) with Recursive Least-Squares (RLS) algorithm. The low convergence rate, good tracking, low miss-adjustment, and good stability are expected capabilities of this filter, which depend highly to value of forgetting factor (0<λ<1). As, with a λ very close to one, stability of the filter is increased due to low misadjustment, but its tracking capabilities is reduced and consequently the OA will remained in the EEG signal even after filtering. To preserve stability of the AF-RLS and improve its tracking, a new configuration of two AF-RLS in the wavelet domain is proposed for applying on approximation and detail coefficients. The proposed algorithm is compared with two older AF-RLS in the time domain (AF-T) and Wavelet based AF using approximation coefficients (WAF-A). Simulation results demonstrate the effectiveness of the proposed algorithm in term of the OA removal and preserving background EEG signals. Also some performance criteria such as visual comparison in the time domain, correlation coefficient and artifact to signal ratio are employed as evidence of this achievement. The proposed algorithm can be implemented in the real-time applications due to fast processing speed.