Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control

Abstract

Efficient action control is indispensable for goal-directed behaviour. Different theories have stressed the importance of either attention or response selection sub-processes for action control. Yet, it is unclear to what extent these processes can be identified in the dynamics of neurophysiological (EEG) processes at the single-trial level and be used to predict the presence of conflicts in a given moment. Applying deep learning, which was blind to cognitive theory, on single-trial EEG data allowed to predict the presence of conflict in ~95% of subjects ~33% above chance level. Neurophysiological features related to attentional and motor response selection processes in the occipital cortex and the superior frontal gyrus contributed most to prediction accuracy. Importantly, deep learning was able to identify predictive neurophysiological processes in single-trial neural dynamics. Hence, mathematical (artificial intelligence) approaches may be used to foster the validation and development of links between cognitive theory and neurophysiology of human behavior.

Vahid et al. use a deep-learning approach to analyze single-trial EEG data to examine theories on action control. Their approach enables the identification of spatial and temporal neurophysiological features that are predictive of the response control during the Simon task. The results confirm cognitive theory-driven approaches on the relationship between neurophysiology and human behavior.