EEG Classification for Motor Imagery Mental Tasks Using Wavelet Signal Denoising

Abstract

Brain-Computer Interfaces (BCIs) are an approach that enables humans to interact with their surroundings by brain generated control signals. Electroencephalographic (EEG) signals that records electrical activity through the scalp might contain superfluous artifacts suppressing some valuable information. Thus the EEG signal denoising is an important stage of the EEG data analyses. The paper presents an experimental comparison of several classification approaches for 2-class motor imagery EEG data classification and explores the influence of wavelet signal denoising on the classification accuracy.