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Assessing Meditation State Using EEG-based Permutation Entropy Features

Yupeng Han, Weichen Huang, Haiyun Huang, Jing Xiao, Yuanqing Li

2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics July 1, 2020 DOI: 10.1109/aim43001.2020.9158980 (opens in new tab)

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Study at a glance

AI-extracted from the abstract
Characteristics Experimental classification study Peer reviewed
Sample size 40
Population Twenty advanced yogis with more than three years of yoga experience and twenty nonmeditators
Duration 7 trials, each with 3 minutes per state
Measures Permutation entropy (PE) features, Fisher's ratio, three-class SVM classifier
Topics Meditation
Key findings Permutation entropy features from EEG frequency bands distinguished meditation, attention, and relaxation states with average offline three-class SVM accuracies of 74.31% for advanced yogis and 62.16% for nonmeditators. The authors suggest these features could be useful for meditation-related brain-computer interfaces.

Abstract

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).

Comparable studies

Other experimental studies on meditation, most cited first.

Study Year Design Participants
The benefits of being present: Mindfulness and its role in psychological well-being. Cancer patients (clinical intervention study) 2003 Mixed methods: scale development, correlational, quasi-experimental, laboratory, experience-sampling, and clinical intervention study
Loving-kindness meditation increases social connectedness. 2008 Controlled laboratory experiment
Mindfulness and mind-wandering: Finding convergence through opposing constructs. 2012 Two studies: correlational study and experimental study
Mental Training Enhances Attentional Stability: Neural and Behavioral Evidence 2009 Experimental study
Mindfulness meditation and substance use in an incarcerated population. Incarcerated population 2006 Quasi-experimental with treatment-as-usual control condition

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