Eye blink artifact rejection in single-channel electroencephalographic signals by complete ensemble empirical mode decomposition and independent component analysis
To study an eye blink artifact rejection scheme from single-channel electroencephalographic (EEG) signals has been now a major challenge in the field of EEG signal processing. High removal performance is still needed to more strictly investigate pattern of EEG features. This paper proposes a new eye blink artifact rejection scheme from single-channel EEG signals by combining complete ensemble empirical mode decomposition (CEEMD) and independent component analysis (ICA). We compare the separation performance of our proposed scheme with existing schemes (wavelet-ICA, EMD-ICA, and EEMD-ICA) though real-life data by using signal-to-noise ratio. As a result, CEEMD-ICA showed high performance (11.86 dB) than all other schemes (10.78, 10.59, and 11.30 dB) in the ability of eye blink artifact removal.