Removing ocular artifacts from mixed EEG signals with FastKICA and DWT

Abstract

Ocular movements are inevitable in electroencephalograme (EEG) collection, and the resulting Ocular Artifact (OA) becomes one of the main interferences of EEG due to its great amplitude. Many methods have been proposed to remove OA from EEG recordings based on Blind Source Separation (BSS) algorithm. Often regression is performed in time or frequency domain by completely deleting the OA components. This can cause the overestimation of OA and the information loss of EEG, because EEG and electrooculogram (EOG) mix or spread bidirectionally. Furthermore, there exists a variety of noises, except for OA, and interference coupling in EEG, this also affects the OA removal performance, such as the robustness and anti-interference ability. Here, we propose a novel and generally applicable method, denoted as FKD, for removing OA from mixed EEG signals with the Fast Kernel Independent Component analysis (FastKICA) and Discrete Wavelet Transform (DWT). In two cases of linear and nonlinear mixed models, many experiments are conducted with Brain Computer Interface (BCI) data set. The experiment results show that FKD has good performance comparing with other BBS-based OA removal methods, and it is more acceptable in actual BCI system.