Artifacts removal in EEG signal using a new neural network enhanced adaptive filter

EEG signal is an important clinical tool for diagnosing, monitoring, and managing neurological disorders. This signal is often affected by a variety of large signal contaminations or artifacts, which reduce its clinical usefulness. In this paper, a new adaptive FLN-RBFN-based filter is proposed to cancel the three most serious contaminants, i.e. ocular, muscular and cardiac artifacts from EEG signal. The basic method used in this paper for the elimination of artifacts is adaptive noise cancellation (ANC). The results demonstrate the effectiveness of the proposed technique in extracting the desired EEG component from contaminated EEG signal.