Modified Step Size Least Mean Fourth Algorithm for Artifact Elimination From EEG Signals

Electroencephalography (EEG) is widely utilized in Brain-Computer Interface (BCI) applications for detecting and interpreting brain activity to control external devices. In this work, MSS-LMF is suggested to excision of unwanted signals. The non-stationary signals are estimated using two reference signals. The suggested technique is used to estimate the reference signals, which are then removed from the raw data in order to produce the clean EEG signals. Synthetic and experimental data are both used in the studies. When compared to other approaches, the simulation results demonstrated that the suggested algorithm performed better. Root mean square is evaluated to show the performance of algorithm. The correlation coefficient has obtained 98.32%.