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Ongoing EEG artifact correction using blind source separation
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DOI：10.1016/j.clinph.2023.12.133.png)
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Objective: Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or braincomputer interfaces can be severely hampered by the presence of artifacts. The aim of this study is to describe and evaluate a fast automatic algorithm for ongoing correction of artifacts in continuous EEG recordings, which can be applied offline and online. Methods: The automatic algorithm for ongoing correction of artifacts is based on fast blind source separation. It uses a sliding window technique with overlapping epochs and features in the spatial, temporal and frequency domain to detect and correct ocular, cardiac, muscle and powerline artifacts. Results: The approach was validated in an independent evaluation study on publicly available continuous EEG data with 2035 marked artifacts. Validation confirmed that 88% of the artifacts could be removed successfully (ocular: 81%, cardiac: 84%, muscle: 98%, powerline: 100%). It outperformed state-of-theart algorithms both in terms of artifact reduction rates and computation time. Conclusions: Fast ongoing artifact correction successfully removed a good proportion of artifacts, while preserving most of the EEG signals. Significance: The presented algorithm may be useful for ongoing correction of artifacts, e.g., in online systems for epileptic spike and seizure detection or brain-computer interfaces. (c) 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
Keyword:
Online artifact removal
Electroencephalogram
Blind source separation
Independent component analysis
Brain-computer interface
Epileptic spike and seizure detection
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