Mapping scalp topographies of rhythmic EEG activity using temporal decorrelation based constrained ICA

Abstract
Independent component analysis (ICA) methods are being increasingly applied to the analysis of electromagnetic (EM) brain signals. However, these powerful techniques still generally require subjective a posteriori analysis in order to visualise neurophysiologically meaningful components in the outputs. Standard implementations of ICA are restrictive mainly due to the square mixing assumption (i.e., as many sources as measurement channels) - this is especially so with large multichannel recordings. There are many instances in neurophysiological analysis where there is strong a priori information about the signals being sought; as in tracking the changing scalp topographies of rhythmic activities. Through constraining the ICA solution it is possible to extract signals that are statistically independent, yet which are similar to some reference signal which incorporates the a priori information. We demonstrate this method on a multichannel recording of an epileptiform electroencephalogram (EEG), where we automate the repeated simultaneous extraction of both rhythmic seizure activity, as well as alpha-band activity, over an epoch of EEG. Subjective analysis of the results shows scalp topographies with realistic spatial distributions which conform to our neurophysiologic expectations. This work shows that constraining ICA can be a very useful technique, especially in automated systems and we demonstrate that this can be successfully applied to EM brain signal analysis.