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

Baoxiang Shang; Feiyan Duan; Ruiqi Fu; Junling Gao; Hinhung Sik; Xianghong Meng; Chunqi Chang

doi:10.3389/fnhum.2023.1033420

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

Front Hum Neurosci. 2023 Aug 31:17:1033420. doi: 10.3389/fnhum.2023.1033420. eCollection 2023.

Authors

Baoxiang Shang^#^{1

2}, Feiyan Duan^#^{1

3}, Ruiqi Fu⁴, Junling Gao⁵, Hinhung Sik⁵, Xianghong Meng², Chunqi Chang¹

Affiliations

¹ School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China.
² Department of Neurosurgery, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China.
³ Deepbay Innovation Technology Corporation Ltd., Shenzhen, China.
⁴ Shenzhen Key Laboratory of Smart Healthcare Engineering, Southern University of Science and Technology, Shenzhen, China.
⁵ Buddhist Practice and Counselling Science Lab, Centre of Buddhist Studies, The University of Hong Kong, Hong Kong, Hong Kong SAR, China.

^# Contributed equally.

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.

Keywords: convolutional neural networks (CNN); deep learning; electroencephalogram (EEG); filter bank common spatial pattern (FBCSP); meditation state classification; mindfulness-based stress reduction (MBSR); state and trait characteristics.

Grants and funding

This research was funded by the National Natural Science Foundation of China (No. 61971289), the Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions (No. 2023SHIBS0003), SZU Top Ranking Project (No. 86000000210), and the Sanming Project of Medicine in Shenzhen “Multidisciplinary Epilepsy Diagnosis and Treatment Team of Professor Wang Yuping from Xuanwu Hospital Capital Medical University” (SZSM2020006).