Space-Time Sparse Reconstruction for Magneto-/Electroencephalography
Andrew K. Bolstad, B.D. Van Veen, Robert Marek Nowak · 2008
This article presents a new spatio-temporal method for M/EEG source reconstruction based on the assumption that only a small number of events, localized in space and/or time, are responsible for the measured signal. Each space-time event is represented using a basis function expansion which reflects the most relevant (or measurable) features of the signal. The basis function expansion effectively models sources which span local regions of cortex and occupy specific frequency bands and/or time windows. This model of neural activity leads naturally to a Bayesian likelihood function which balances the model fit to the data with the complexity of the model, where the complexity is related to the number of included events. A novel Expectation-Maximization algorithm which maximizes the likelihood function is presented. The new method is shown to be effective on several MEG simulations of neurological activity as well as data from a self-paced finger tapping experiment.