Key result
Fast adaptive blind source separation cuts short EEG computing time ~80% versus state-of-the-art methods.
Why the study?
Existing artifact removal methods cannot guarantee both effectiveness and efficiency for short-term few-channel EEG recordings.
The proposed NA-FMEMD and CCA-based method significantly reduces computing time for artifact removal in short-term few-channel EEG recordings, making it suitable for real-time processing.
May enable real-time artifact removal in few-channel EEG; leaves open prospective clinical validation.
Artifacts rejection is crucial to electroencephalogram (EEG) application. Short-term few-channel EEG (e.g., in real-time detection of stress level and motor imagery) brings new challenges for removing artifacts due to less data. Existing artifact removal methods cannot guarantee both effectiveness and efficiency for removing artifacts from short-term few-channel EEG recordings. Consequently, we propose a fast adaptive multidimensional sub-bands blind source separation method to remove artifacts from short-term few-channel EEG recordings effectively and efficiently. Firstly, noise-assisted fast multivariate empirical mode decomposition (NA-FMEMD), as a fast adaptive multidimensional sub-bands decomposition method, is employed to decompose short-term few-channel EEG recordings into multidimensional sub-bands. Then canonical correlation analysis (CCA), as a blind source separation method, is used to estimate artifacts-related and EEG-related sources. Finally, EEG-related sources are intelligently selected and reconstructed as clean EEG recordings. The results demonstrate that our method takes at least 5 times less computing time for 2-s few-channel EEG recordings than state-of-the-art methods with similar effectiveness, using the same computer and software. Therefore, our method enhances the efficiency of removing artifacts from short-term few-channel EEG recordings while ensuring effectiveness, and it is more suitable for real-time processing.
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Zhang et al. (2021) studied this question. Fast adaptive multidimensional sub-bands blind source separation method (NA-FMEMD and CCA) vs. State-of-the-art methods was evaluated on Computing time for 2-s few-channel EEG recordings (at least 5 times less computing time). A fast adaptive multidimensional sub-bands blind source separation method required at least 5 times less computing time for 2-s few-channel EEG recordings compared to state-of-the-art methods.
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