Classification of Event-Related Potentials associated with response errors in actors

Event-Related Potentials (ERPs) provide non-invasive measurements of the electrical activity on the scalp related to the processing of stimuli and preparation of responses by the brain. In this paper, an ERP-signal classification method capable of discriminating between ERPs of correct and incorrect responses of actors is proposed. A number of histogram-related features were calculated from each ERP-signal and the most significant ones were extracted using the Sequential Forward Floating Selection algorithm along with the Fuzzy C-Means clustering algorithm. The Fuzzy C-Means algorithm was also used for the classification task. The approach yielded classification accuracy 93.75% for the actors’ correct and incorrect responses. The proposed ERP-signal classification method provides a promising tool to study error detection and observational-learning mechanisms in joint-action research and may foster the future development of systems capable of automatically detecting erroneous actions in human-human and human-artificial agent interactions.