期刊
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
卷 67, 期 1, 页码 187-191出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSII.2019.2903648
关键词
Electroencephalography; Electromyography; Muscles; Correlation; Matrix decomposition; Covariance matrices; Underdetermined; joint canonical polyadic decomposition; artifact removal; auto-correlations
资金
- National Natural Science Foundation of China [81571760, 61501164, 61871411]
- Natural Sciences and Engineering Research Council of Canada
Electroencephalography (EEG) recordings are often contaminated by artifacts from electromyogram (EMG). This artifact not only affects the visual analysis but also strongly impedes its various usages in biomedical research. With a sufficient number of EEG recordings, numerous blind source separation (BSS) methods can be applied to suppress or remove such EMG artifacts. However, in many practical applications (e.g., ambulatory health-care monitoring), the number of EEG sensors is often limited, while conventional BSS methods (e.g., independent component analysis) may fail to work in such cases. Considering the increasing need for acquiring EEG signals in ambulatory environments, we propose a novel underdetermined joint BSS method to remove EMG artifacts from EEG data with a limited number of EEG sensors. The performance of the proposed method is evaluated through numerical simulations in which EEG recordings are contaminated with muscle artifacts. The results demonstrate that the proposed method can effectively remove muscle artifacts while preserving EEG signals successfully.
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