Separation of ERP and Noise Subspaces in EEG Data without Whitening

In this article, a method for separating linear subspaces of time-locked brain responses and other noise sources in multichannel electroencephalography data is proposed. The components related to time-locked and noise subspaces are distinguished by method based on different behavior they experience after traditional averaging. The actual separation of the two subspaces is performed without whitening by maximizing/minimizing the same criterion. The detailed derivation of the method is given, and the results of the method's application to simulated and real EEG datasets are studied. The possibilities of improving the results are also discussed.