The detection of cognitive state transitions by stability changes in event-related cortical field potentials

Abstract

Cognitive tasks are characterized by sequences of stable states, which are thought to reflect distinct steps of neurocognitive processing. Here, we investigate a stability measure, derived from adaptive multivariate autoregressive (AMVAR) modeling of cortical field potentials, as an index for relating changes in large-scale neural activity to changes in cognitive state. We show that this stability measure can be used to decide the optimal window length for AMVAR modeling and to detect state transitions related to external sensory or motor events. By using this approach, we demonstrate clear differentiation of GO and NO-GO processes in a macaque monkey performing a visuomotor pattern discrimination task. Moreover, we are able to identify regional differences in state transitions, apparently reflecting regional information processing differences.