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 100 ms. 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.
Highlights
Magnetoencephalography (MEG) and electroencephalography (EEG) data have the ability to provide direct, non-invasive measurements of neuronal activity
We have presented a new adaptive time-varying approach to source reconstruction, underpinned by a Hidden Markov Model (HMM)
The HMM infers when in time particular states occur, allowing intelligent pooling of data over distinct and potentially short-lived periods in time. This is used to compute timevarying data covariance matrices for use in beamforming, resulting in a source reconstruction approach that can tune its spatial filtering properties to that which is required at different points in time
Summary
Magnetoencephalography (MEG) and electroencephalography (EEG) data have the ability to provide direct, non-invasive measurements of neuronal activity This is providing new insights into the dynamics of brain activity at the systems level, most recently using magnetoencephalography (MEG) to investigate networks of oscillatory activity in the human brain (Brookes et al, 2011; de Pasquale et al, 2010; Hipp et al, 2012; Luckhoo et al, 2012). The beamformer spatial filter weights are determined from the forward model, i.e. the lead field matrix, and an estimate of the sensor data covariance. The accuracy of this data covariance matrix estimation is crucial, and key to the beamformer's ability to spatially adapt to the data (Brookes et al, 2008)
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