Key result
The CEEMD-ICA scheme achieved a higher signal-to-noise ratio (11.86 dB) for eye blink artifact removal in single-channel EEG signals compared to existing schemes (10.59 to 11.30 dB).
The proposed CEEMD-ICA scheme outperforms existing methods in removing eye blink artifacts from single-channel EEG signals.
May enhance EEG signal quality for monitoring; leaves open validation before clinical or research adoption.
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.
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Kanoga et al. (2015) studied Eye blink artifacts in single-channel EEG signals. Complete ensemble empirical mode decomposition and independent component analysis (CEEMD-ICA) vs. wavelet-ICA, EMD-ICA, and EEMD-ICA was evaluated on Signal-to-noise ratio. The CEEMD-ICA scheme achieved a higher signal-to-noise ratio (11.86 dB) for eye blink artifact removal in single-channel EEG signals compared to existing schemes (10.59 to 11.30 dB).
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