Key result
A novel underdetermined joint blind source separation method effectively removed muscle artifacts from simulated EEG data with a limited number of sensors while preserving EEG signals.
Why the study?
Conventional blind source separation methods may fail to remove electromyogram artifacts from EEG data when the number of EEG sensors is limited, such as in ambulatory monitoring.
A novel underdetermined joint BSS method effectively removes EMG artifacts from EEG data with a limited number of sensors in simulation.
Hypothesis-generating for EMG artifact removal in EEG; requires validation beyond numerical simulations before clinical use.
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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Zou et al. (2019) studied EEG recordings contaminated by EMG artifacts. Underdetermined joint blind source separation (BSS) method vs. Conventional BSS methods (e.g., independent component analysis) was evaluated on Removal of muscle artifacts while preserving EEG signals. A novel underdetermined joint blind source separation method effectively removed muscle artifacts from simulated EEG data with a limited number of sensors while preserving EEG signals.
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