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.Expand

This paper studies input signals for the identification of nonlinear discrete-time systems modeled via a truncated Volterra series representation.Expand

A constructive procedure is presented for designing low rank detectors which maximize the divergence between hypotheses about the covariance structure of Gaussian signals.Expand

We show that in Gauss-Gauss detection J-divergence is a function of squared canonical correlations and hence is invariant to nonsingular transformations of the data channels.Expand

A state-space formulation is introduced for estimating multivariate autoregressive (MVAR) models of cortical connectivity from noisy, scalp-recorded EEG.Expand