Non-negative matrix factorization for single-channel EEG artifact rejection

New applications of Electroencephalographic recording (EEG) pose new challenges in terms of artifact removal. In our work we target applications where the EEG is to be captured by a single electrode and a number of additional lightweight sensors are allowed. Thus, this paper introduces a new method for artifact removal for single-channel EEG recordings using nonnegative matrix factorisation (NMF) in a Gaussian source separation framework. We focus the study on ocular artifacts and show that by properly exploiting prior information on the latter, through the analysis of electrooculographic recordings, our artifact removal results on single-channel EEG are comparable to the results obtained with the classic multi-channel Independent Component Analysis technique.