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.
Meditation benefits physical and mental health, but walking meditation is understudied. This work recorded EEG signals from 7-Day Zen participants in four states: sitting around, strolling, sitting meditation, and walking meditation. Rhythm-based EEG features were extracted and calibrated to classify the states. Personalized calibration methods effectively addressed individual differences in EEG signals, and the system identified the four states with high accuracy, offering insight into neural activity during different meditation forms.