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

Abstract

Introduction: Prior Go/NoGo studies have localized specific regions and EEG spectra for which traditional approaches have distinguished between Go and NoGo conditions. A more detailed characterization of the spatial distribution and timing of the synchronization of frequency bands would contribute substantially to the clarification of neural mechanisms that underlie performance of the Go/NoGo task. Methods: The present study used a machine learning approach to learn the features that distinguish between ERSPs involved in selection and inhibition in a Go/NoGo task. A single-layer neural network classifier was used to predict task conditions for each subject to characterize ERSPs associated with Go versus NoGo trials. Results: The final classifier accurately identified individual task conditions at an overall rate of 92%, estimated by fivefold cross-validation. The detailed accounting of EEG time–frequency patterns localized to brain regions (i.e., thalamus, pre-SMA, orbitofrontal cortex, and superior parietal cortex) corroborates and also elaborates upon previous findings from fMRI and EEG studies, and expands the information about EEG power changes in multiple frequency bands (i.e., primarily theta power increase, alpha decreases, and beta increases and decreases) within these regions underlying the selection and inhibition processes engaged in the Go and NoGo trials. Conclusion: 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.