Assessing Meditation State Using EEG-based Permutation Entropy Features

Meditation aims to improve individuals' core psychological capacities, such as attentional and emotional self-regulation. In this study, we experimented to explore the utility of using permutation entropy (PE) features on electroencephalogram (EEG) data to distinguish among states in human meditation, attention, and relaxation. Twenty advanced yogis with above three years yoga experience and twenty nonmeditators were recruited in our experiment. The whole experiment contained 7 trials. During each trial, subjects were requested to maintain meditation, attention, and relaxation state for three minutes in random order, respectively, after the corresponding voice prompted. In the meantime, 30-channel EEG data were collected in real-time. We calculated PE features from different EEG frequency bands and selected the most correlative features using Fisher's ratio technique. After that, we fed the feature vectors into a three-class SVM classifier. Twenty yogis and twenty non-meditators achieved average offline classification accuracies of 74.31% and 62.16%, respectively. These results indicated that PE features could be useful in discriminating meditation state and might bring new insight in building meditation-related Brain-Computer Interfaces (BCIs).