Tracking epileptiform activity in the multichannel ictal EEG using spatially constrained independent component analysis

Blind source separation (BSS) methods such as independent component analysis (ICA) are increasingly being used in biomedical signal processing for decomposition of multivariate time-series, such as the multichannel electroencephalogram (EEG), into a set of underlying sources, some of which may reflect clinically relevant neurophysiological activity such as epileptic seizures or spikes. Tracking and detecting signals of interest fundamentally requires at least some a priori knowledge or assumptions regarding the spatial and/or temporal characteristics of the target sources. While such prior information is conventionally used during post-processing, it seems equally sensible to incorporate any available information into the data decomposition process from the outset. This work presents an alternative approach to source tracking in multichannel EEG, which exploits prior knowledge of the spatial topographies of the scalp voltage distributions associated with the target sources. The predetermined target topographies are used in conjunction with spatially constrained ICA to extract target source waveforms which are uncontaminated by contributions from coactive and spatially correlated brain and artifact sources. These signals can then be further analyzed in terms of their morphological, spectral or statistical properties. As illustrated in the context of epileptiform EEG, this method is useful for tracking seizures.