Classifying creativity: Applying machine learning techniques to divergent thinking EEG data

• Published in: NeuroImage (Orlando, Fla.) Vol. 219; p. 116990
• Main Authors: Stevens, Carl E., Zabelina, Darya L.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.10.2020; Elsevier Limited; Elsevier
• Subjects: Accuracy; Adolescent; Algorithms; Alpha; Alpha Rhythm - physiology; AUT; Brain - physiology; Brain Mapping; Brain research; Classification; Cognition & reasoning; Creativity; Decision making; Discriminant analysis; EEG; Electroencephalography; Female; Humans; Learning algorithms; Machine Learning; Male; Researchers; Support vector machines; Wavelet transforms; Young Adult; AUT; Alpha; EEG; Creativity; Classification
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.116990

Prior research has shown that greater EEG alpha power (8–13 ​Hz) is characteristic of more creative individuals, and more creative task conditions. The present study investigated the potential for machine learning to classify more and less creative brain states. Participants completed an Alternate Uses Task, in which they thought of Normal or Uncommon (more creative) uses for everyday objects (e.g., brick). We hypothesized that alpha power would be greater for Uncommon (vs. Common) uses, and that a machine learning (ML) approach would enable the reliable classification data from the two conditions. Further, we expected that ML would be successful at classifying more (vs. less) creative individuals. As expected, alpha power was significantly greater for the Uncommon than for the Normal condition. Using spectrally weighted common spatial patterns to extract EEG features, and quadratic discriminant analysis, we found that classification accuracy for the two conditions varied widely among individuals, with a mean of 63.9%. For more vs. less creative individuals, 82.3% classification accuracy was attained. These findings indicate the potential for broader adoption of machine learning in creativity research. •Two conditions in an alternate uses task required 2 different levels of creativity.•Machine learning was used to classify more and less creative brain states.•Machine learning was used to classify more and less creative individuals.•Findings indicate the potential utility of machine learning in creativity research.