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.