Abstract
Our understanding of the dynamics of neuronal activity in the human brain remains limited, due in part to a lack of adequate methods for reconstructing neuronal activity from noninvasive electrophysiological data. Here, we present a novel adaptive time-varying approach to source reconstruction that can be applied to magnetoencephalography (MEG) and electroencephalography (EEG) data. The method is underpinned by a Hidden Markov Model (HMM), which infers the points in time when particular states re-occur in the sensor space data. HMM inference finds short-lived states on the scale of 100ms. Intriguingly, this is on the same timescale as EEG microstates. The resulting state time courses can be used to intelligently pool data over these distinct and short-lived periods in time. This is used to compute time-varying data covariance matrices for use in beamforming, resulting in a source reconstruction approach that can tune its spatial filtering properties to those required at different points in time. Proof of principle is demonstrated with simulated data, and we demonstrate improvements when the method is applied to MEG.