Proceedings of the Annual Meeting of the Cognitive Science Society

Prior research has shown that greater EEG alpha power (8-13 Hz) is characteristic of greater creativity. This study investi-gates the potential for machine learning to classify more and less creative brain states. Participants completed an alternateuse task, in which they thought of normal or uncommon (more demanding) uses for everyday objects (e.g., brick). Wehypothesized that alpha power and reaction time would be greater for uncommon uses, and that a trained machine learningmodel would be able to reliably classify data from the two conditions. Participants responded much faster in the normalcondition, compared to uncommon; alpha was significantly greater for the uncommon condition; and 73.3% classifica-tion accuracy was attained when a trained model was applied to new data. Future research will attempt to implementneurofeedback training to maintain optimally creative states.