Identification of selection and inhibition components in a Go/NoGo task from EEG spectra using a machine learning classifier.

This time-frequency-based classifier extends previous spatiotemporal findings and provides information about neural mechanisms underlying selection and inhibition processes engaged in Go and NoGo trials, respectively. This neural network classifier can be used to assess time-frequency patterns from an individual subject and thus may offer insight into therapeutic uses of neuromodulation in neural dysfunction.