Multivariate EMD based approach to EOG artifacts separation from EEG

Measured electroencephalography (EEG) signals can be contaminated with other electrophysiological signal sources. This contamination decreases accuracy of neuroengineering applications such as brain computer interfaces. This paper focuses on the removal of electrooculography (EOG) that strongly appears in frontal electrodes EEG. To develop an EOG removal algorithm, we propose to utilize recently developed a multivariate extension of empirical mode decomposition (EMD) called MEMD. MEMD decomposes a multichannel signal into a set of intrinsic mode functions (IMF), and the number of IMFs is identical among the channels. We establish a criterion for choosing IMFs to separate an EOG-related component from the observed signal. Numerical examples confirm the proposed approach extracts EOG component better comparing to conventional blind source separation methods.