Trial-by-trial detection of cognitive events in neural time-series

Abstract

Measuring the time-course of neural events that make up cognitive processing is crucial to understand the relation between brain and behavior. To this aim, we formulated a method to discover a trial-wise sequence of events in multivariate neural signals such as electro- or magneto-encephalograpic (E/MEG) recordings. This sequence of events is assumed to be represented by multivariate patterns in neural time-series, with the by-trial inter-event intervals following probability distributions. By estimating event-specific multivariate patterns, and between-event time interval distributions, the method allows to recover the by-trial location of brain responses. We demonstrate the properties and robustness of this hidden multivariate pattern (HMP) method through simulations, including robustness to low signal-to-noise ratio, as typically observed in EEG recordings. The applicability of HMP is illustrated using three previously published datasets. We show how HMP provides, for any experiment or condition, an estimate of the number of events, the sensors contributing to each event (e.g. EEG scalp topography), and the intervals between each event. Traditional exploration of tasks cognitive structures and electrophysiological analyses can thus be enhanced by HMP estimates.