Feature Representation for Meditation State Classification in EEG Signal
International Conference on Information Technology in Medicine and Education November 1, 2021 Min Huang, Lizhen Ye, Junze Chen et al.
Meditation in walking posture is less studied than sitting meditation. To distinguish between sitting and walking meditation states, a classification framework was developed using EEG signal features and a random forest classifier. Single-modal features—original power, power ratio, and non-linear dynamics—were compared. The original power feature outperformed non-linear dynamics. A multi-modal feature combining all single-modal features achieved the highest accuracy in identifying the two meditation states.