Classification of mindfulness experiences from gamma-band effective connectivity: Application of machine-learning algorithms on resting, breathing, and body scan.

Journal: Computer methods and programs in biomedicine
PMID: 39369588

Abstract

BACKGROUND AND OBJECTIVE: Practicing mindfulness is a mental process toward interoceptive awareness, achieving stress reduction and emotion regulation through brain-function alteration. Literature has shown that electroencephalography (EEG)-derived connectivity possesses the potential to differentiate brain functions between mindfulness naïve and mindfulness experienced, where such quantitative differentiation could benefit telediagnosis for mental health. However, there is no prior guidance in model selection targeting on the mindfulness-experience prediction. Here we hypothesized that the EEG effective connectivity could reach a good prediction performance in mindfulness experiences with brain interpretability.

Authors

  • Ai-Ling Hsu
    Department of Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
  • Chun-Yu Wu
    Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.
  • Hei-Yin Hydra Ng
    Research Center for Education and Mind Sciences, College of Education, National Tsing Hua University, Hsinchu, Taiwan; Department of Educational Psychology and Counseling, College of Education, National Tsing Hua University, Hsinchu, Taiwan.
  • Chun-Hsiang Chuang
  • Chih-Mao Huang
  • Changwei W Wu
    Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, New Taipei, Taiwan; Research Center of Sleep Medicine, Taipei Medical University Hospital, Taipei, Taiwan. Electronic address: sleepbrain@tmu.edu.tw.
  • Yi-Ping Chao
    Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan City 333323, Taiwan.

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