Author: Ai-Ling Hsu1, Chun-Yu Wu2, Hei-Yin Hydra Ng3, Chun-Hsiang Chuang4, Chih-Mao Huang5, Changwei W Wu6, Yi-Ping Chao7
Affiliation:
1 Department of Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
2 Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.
3 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.
4 Research Center for Education and Mind Sciences, College of Education, National Tsing Hua University, Hsinchu, Taiwan; Institute of Information Systems and Applications, College of Electrical Engineering and Computer Science, National Tsing Hua University, Hsinchu, Taiwan.
5 Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
6 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.
7 Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan; Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan. Electronic address: yiping@mail.cgu.edu.tw.
Conference/Journal: Comput Methods Programs Biomed
Date published: 2024 Sep 28
Other:
Volume ID: 257 , Pages: 108446 , Special Notes: doi: 10.1016/j.cmpb.2024.108446. , Word Count: 345
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.
Methods:
We aimed at probing direct Directed Transfer Function (dDTF) to classify the participants' history of mindfulness-based stress reduction (MBSR), and aimed at optimizing the prediction accuracy by comparing multiple machine learning (ML) algorithms. Targeting the gamma-band effective connectivity, we evaluated the EEG-based prediction of the mindfulness experiences across 7 machine learning (ML) algorithms and 3 sessions (i.e., resting, focus-breathing, and body-scan).
Results:
The support vector machine and naïve Bayes classifiers exhibited significant accuracies above the chance level across all three sessions, and the decision tree algorithm reached the highest prediction accuracy of 91.7 % with the resting state, compared to the classification accuracies with the other two mindful states. We further conducted the analysis on essential EEG channels to preserve the classification accuracy, revealing that preserving just four channels (F7, F8, T7, and P7) out of 19 yielded the accuracy of 83.3 %. Delving into the contribution of connectivity features, specific connectivity features predominantly located in the frontal lobe contributed more to classifier construction, which aligned well with the existing mindfulness literature.
Conclusion:
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.
Keywords: Decision tree; Effective connectivity; Electroencephalography (EEG); Machine learning; Mindfulness; Mindfulness-based stress reduction (MBSR).
PMID: 39369588 DOI: 10.1016/j.cmpb.2024.108446
