Mixed step size normalized least mean fourth adaptive algorithm for artifact elimination from raw EEG signals

Abstract

In this paper, a novel algorithm is proposed to eliminate the ocular artifacts (OAs) using mixed step size normalized least mean fourth adaptive algorithm from the raw EEG signals. Here, the fourth-order power optimization technique is used to eliminate the OAs effectively. The reference signals, such as vertical electrooculogram and horizontal electrooculogram, are recorded separately. Thereby, these signals are processed by the FIR filter, whose coefficients are adaptively updated at each iteration by using the proposed algorithm. The processed signals are subtracted from the raw EEG signals in order to obtain clean EEG signals. In addition, the mathematical model for the mean square deviation analysis is provided and compared with conventional methods like combined step size normalized least mean squares and variable parameter normalized mixed norm adaptive algorithm. From the simulation results, it is found that the proposed algorithm exhibits better performance in terms of minimum mean square deviation.