1. Ongoing EEG artifact correction using blind source separation
- Author
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Ille, Nicole, Nakao, Yoshiaki, Shumpei, Yano, Taura, Toshiyuki, Ebert, Arndt, Bornfleth, Harald, Asagi, Suguru, Kozawa, Kanoko, Itabashi, Izumi, Sato, Takafumi, Sakuraba, Rie, Tsuda, Rie, Kakisaka, Yosuke, Jin, Kazutaka, and Nakasato, Nobukazu
- Subjects
Quantitative Biology - Quantitative Methods ,Electrical Engineering and Systems Science - Signal Processing ,Statistics - Methodology - Abstract
Objective: Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer 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-the-art 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., Comment: 16 pages, 4 figures, 3 tables
- Published
- 2023
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