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New applications of Electroencephalographicrecording (EEG) require light and easy-to-handle equipment involving powerful algorithms of artifact removal. In our work, we exploit informed source separation methods for artifact removal in EEG recordings with a low number of sensors, especially in the extreme case of single-channel recording, by exploiting prior knowledge from auxiliary lightweight sensors...
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