Nonlinear EEG Analysis for Distinguishing Mind Wandering and Focused Attention: A Machine Learning Approach

This study uses nonlinear analysis techniques to distinguish between mind wandering (MW) and focused attention (FA) states using EEG data. EEG recordings from 21 sessions were segmented into intervals of 2, 3, 5, 6, 10, and 15 seconds, and seven nonlinear features were extracted to capture the brain’s dynamic complexity. Machine learning models, including gradient boosting trees, were applied to classify MW and FA states, with the highest accuracy of 75% achieved using 5-second segments.

Frequency-related features, particularly mean frequency and global frequency, were the most important in distinguishing between MW and FA. These findings emphasize the role of nonlinear EEG analysis in understanding the chaotic brain patterns underlying cognitive states. Future work should focus on temporal dynamics and personalized models to improve classification accuracy, with potential applications in cognitive enhancement and mental health.