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A new method for estimating multivariate autoregressive (MVAR) models of cortical connectivity from surface EEG or MEG measurements is presented. Conventional approaches to this problem first attempt to solve the inverse problem to estimate cortical signals and then fit an MVAR model to the estimated signals. Our new approach expresses the measured data in tens of a hidden state equation describing...
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