Automatic eye-blink artifact removal method based on EMD-CCA

Abstract

This research proposes a new hybrid algorithm for automatic removal of eye blink artifact from EEG data based on empirical mode decomposition (EMD) and canonical correlation analysis (CCA). The validity and efficiency of the proposed algorithm is evaluated using correlation coefficient and signal-to-artifact ratio (SAR) and the proposed algorithm is also compared with other popular eye blink artifact removal techniques (CCA, ICA, EMD-ICA) on simulated EEG data of two channels. From the simulation results, the average correlation coefficients for the EEG channels are obtained as 0.908 and 0.864 respectively. The SAR of the EEG signal also improved from 2.2 dB to 6.0 dB after correction using our proposed method. Compared to other eye blink artifact removal techniques, our proposed method has two benefits. Firstly, no visual inspection is required to detect the eye blink artifact components. Secondly, computational assessment of corrected EEG waveforms reveals that the proposed algorithm retrieves the EEG data by removing the eye blink artifacts reliably.