Classification of mindfulness experiences from gamma-band effective connectivity: Application of machine-learning algorithms on resting, breathing, and body scan
In the present study, we initiated a milestone of developing an EEG-based classifier to detect a person's mindfulness experience objectively. The prediction accuracy of the decision tree was optimal to differentiate the mindfulness experiences using the local resting-state EEG data. The suggested algorithm and key channels on the mindfulness-experience prediction may provide guidance for predicting mindfulness experiences using the EEG-based classification embedded in future wearable neurofeedback systems or plausible digital therapeutics.
Authors
- Hei‐Yin Hydra Ng (ORCID: https://orcid.org/0000-0001-6547-7211)
- Ai‐Ling Hsu (ORCID: https://orcid.org/0000-0002-6801-4658)
- Chih‐Mao Huang (ORCID: https://orcid.org/0000-0002-6209-2575)
- Changwei W. Wu (ORCID: https://orcid.org/0000-0001-8968-9366)
- Yi‐Ping Chao (ORCID: https://orcid.org/0000-0002-1681-5410)
- Chun‐Yu Wu (ORCID: https://orcid.org/0000-0002-9544-8654)
- Chun‐Hsiang Chuang (ORCID: https://orcid.org/0000-0002-5043-8380)
Institutions
- National Yang Ming Chiao Tung University (TW)
- Chang Gung University (TW)
- National Tsing Hua University (TW)
- Taipei Medical University Hospital (TW)
- Linkou Chang Gung Memorial Hospital (TW)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2024-09-28
- DOI
- https://doi.org/10.1016/j.cmpb.2024.108446
- Citations
- 2
- Primary Topic
- Mindfulness and Compassion Interventions
- Type
- article
- Field-Weighted Citation Impact
- 1.28
Funders
- Ministry of Education
- Chang Gung Memorial Hospital, Linkou