EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning

Short-term mindfulness-based stress reduction (MBSR) training produces electroencephalogram (EEG)-detectable state and trait effects. Using convolutional neural networks (deep learning) and support vector machines (SVM) with features from common spatial patterns, classifiers were trained on EEG from 11 novice MBSR practitioners during rest and meditation at early and late training stages. Shallow ConvNet classifiers achieved mix-subject and intra-subject accuracies superior to prior studies for both novice and expert meditators across meditation types. FBCSP+SVM classifiers gave inter-subject accuracies of 68.50% for state effect recognition, 85.00% for trait effect recognition using meditation EEG, and 78.96% using resting EEG. Deep learning outperforms for state effects in novices and is comparable for experts.

Study at a glance

Abstract

Introduction: This study examines the state and trait effects of short-term mindfulness-based stress reduction (MBSR) training using convolutional neural networks (CNN) based deep learning methods and traditional machine learning methods, including shallow and deep ConvNets as well as support vector machine (SVM) with features extracted from common spatial pattern (CSP) and filter bank CSP (FBCSP). Methods: We investigated the electroencephalogram (EEG) measurements of 11 novice MBSR practitioners (6 males, 5 females; mean age 35.7 years; 7 Asians and 4 Caucasians) during resting and meditation at early and late training stages. The classifiers are trained and evaluated using inter-subject, mix-subject, intra-subject, and subject-transfer classification strategies, each according to a specific application scenario. Results: For MBSR state effect recognition, trait effect recognition using meditation EEG, and trait effect recognition using resting EEG, from shallow ConvNet classifier we get mix-subject/intra-subject classification accuracies superior to related previous studies for both novice and expert meditators with a variety of meditation types including yoga, Tibetan, and mindfulness, whereas from FBSCP + SVM classifier we get inter-subject classification accuracies of 68.50, 85.00, and 78.96%, respectively. Conclusion: Deep learning is superior for state effect recognition of novice meditators and slightly inferior but still comparable for both state and trait effects recognition of expert meditators when compared to the literatures. This study supports previous findings that short-term meditation training has EEG-recognizable state and trait effects.