Filling the void - enriching the feature space of successful stopping

The ability to inhibit behavior is crucial for adaptation in a fast changing environment and is commonly studied with the stop signal task. Current EEG research mainly focuses on the N200 and P300 ERPs and corresponding activity in the theta and delta frequency range, thereby leaving us with a limited understanding of the mechanisms of response inhibition. Here, 15 functional networks were estimated from time-frequency transformed EEG recorded during processing of a visual stop signal task. Cortical sources underlying these functional networks were reconstructed, and a total of 45 features, each representing spectrally and temporally coherent activity, were extracted to train a classifier to differentiate between go and stop trials. A classification accuracy of 85.55% for go and 83.85% for stop trials was achieved. Features capturing fronto-central delta- and theta activity, parieto-occipital alpha, fronto-central as well as right frontal beta activity were highly discriminating between trial-types. However, only a single network, comprising a feature defined by oscillatory activity below 12 Hz, was associated with a generator in the opercular region of the right inferior frontal cortex and showed the expected associations with behavioral inhibition performance. This study pioneers by providing a detailed ranking of neural features regarding their information content for stop and go differentiation at the single-trial level, and may further be the first to identify a scalp EEG marker of the inhibitory control network. This analysis allows for the characterization of the temporal dynamics of response inhibition by matching electrophysiological phenomena to cortical generators and behavioral inhibition performanc