Detection of freely moving thoughts using SVM and EEG signals

• Published in: Journal of neural engineering Vol. 22; no. 2; pp. 26021 - 26035
• Main Authors: Nanjappan Jothiraj, Sairamya, Mills, Caitlin, Irving, Zachary C, Kam, Julia W Y
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 19.03.2025
• Subjects: Adult; Attention - physiology; EEG; electroencephalography; Electroencephalography - methods; Female; freely moving thought; Humans; machine learning; Male; mind wandering; Support Vector Machine; Thinking - physiology; Young Adult; electroencephalography; mind wandering; machine learning; freely moving thought; EEG
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2552/adbd77

Objective. Freely moving thought is a type of thinking that shifts from one topic to another without any overarching direction or aim. The ability to detect when freely moving thought occurs may help us promote its beneficial outcomes, such as for creative thinking and positive mood. Thus far, no studies have used machine learning to detect freely moving thought on the basis of ‘objective’ (e.g. neural or behavioral) data. Approach. Our study addresses this gap, using event-related potential (ERP) and spectral features of electroencephalogram (EEG) signals as well as behavioral measures during a simple attention task and machine learning to detect freely moving thought. EEG features were first examined with both inter-subject and intra-subject strategies. Specifically, the statistical and entropy features of the P3 ERP and alpha spectral measures were entered as inputs to the support vector machine. The best combination of EEG features achieving higher classification performance in both strategies were then selected to combine with behavioral features to further enhance classification performance. Main results. Our best performing model has a Matthew’s correlation coefficient and area under the curve of 0.3105 and 0.6665 for inter-subject models and 0.2815 and 0.6407 for intra-subject models respectively. Significance. The above chance level performance in both strategies using EEG and behavioral features shows great promise for machine learning approaches to detect freely moving thought and highlights their potential for real-time prediction in the real world. This has important implications for enhancing creative processes and mood associated with freely moving thought.