Spectral Analysis of EEG Data for Ocular Artifact Removal Using Wavelet Transform Technique

Abstract

The success of image processing enables electroencephalography (EEG) portable devices. It has initiated the step to a new concept like processing a minimum count of EEG channels for health monitoring and brain technical system at low cost. We present an adaptive filtering to effectively remove Ocular Artifact (OA) in EEG data. This removal is based on time-frequency analysis approach which is able to identify and filter automatically present ocular and muscular artifacts embedded in EEG. For the occurrence of slight and heavy artifacts, ocular artifact removal method provides a relative low error compared to lower traditional techniques. The results obtained can be used as a solution in ambulatory healthcare systems, where low count EEG channels or even an individual channel is not available.