Elimination of EOG signals from raw EEG signals using step size based recursive least squares- least mean fourth adaptive algorithm

Abstract

A novel Step Size based Recursive least squares- Least mean fourth(SSRL) adaptive algorithm is proposed as a noise canceller, in this paper, to eliminate the Ocular artifacts(OAs) from the recorded raw EEG signals. Here, both second and fourth-order power optimization algorithms have been used to eliminate the OAs from raw EEG signals. The Reference signals, such as vertical Electrooculogram and horizontal Electrooculogram signals, have been recorded separately.FIR filter has been used to process the reference signals by updating filter coefficients using the proposed SSRL algorithm. Thereby, true EEG signals are obtained by subtracting the processed signals from the raw signals. Moreover, a mathematical model for the mean square deviation analysis is proposed for the SSRL algorithm, which provides better results compared to the conventional methods.