Modified ocular artifact removal technique from EEG by adaptive filtering

Abstract

Electroencephalogram (EEG) is the reflection of brain activity and is widely used in clinical diagnoses and biomedical researches. EEG signals recorded from the scalp contain many artifacts that make its interpretation and analysis very difficult. One major source of artifacts is from eye movements that generate the electrooculogram (EOG). Many applications of EEG such as brain computer interface (BCI) need real time processing of EEG. Adaptive filtering is one of the most efficient methods for removal of ocular artifacts which can be applied in real time. In conventional adaptive filtering, the primary input is measured EEG and the reference inputs are vertical EOG (VEOG) and horizontal EOG (HEOG) signals. In this paper, we have proposed an adaptive filtering approach which includes radial EOG (REOG) signal as a third reference input. We have analyzed the performance of adaptive algorithms using two reference inputs i.e. VEOG and HEOG and three reference inputs i.e. VEOG, HEOG and REOG and it is found that the proposed 3 reference method gives more accurate results than the existing two reference method.