Classification of mindfulness experiences from gamma-band effective connectivity: Application of machine-learning algorithms on resting, breathing, and body scan.
Ai-Ling Hsu, Chun-Yu Wu, Hei-Yin Hydra Ng, Chun-Hsiang Chuang, Chih-Mao Huang, Changwei W Wu, Yi-Ping Chao
Computer methods and programs in biomedicine December 1, 2024 DOI: 10.1016/j.cmpb.2024.108446 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Population | Participants with and without mindfulness-based stress reduction (MBSR) experience |
| Topics | Meditation |
| Keywords | Decision tree Effective connectivity Electroencephalography EEG Machine learning |
| Key findings | The decision tree algorithm reached the highest prediction accuracy of 91.7% for classifying mindfulness experience using resting-state EEG effective connectivity. |
Abstract
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. 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). 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. 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.
Comparable studies
Other observational and cohort studies on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Using Self-Report Assessment Methods to Explore Facets of Mindfulness Undergraduate students | 2006 | Observational cohort | |
| Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders Patients with generalized anxiety disorder or panic disorder with or without agoraphobia | 1992 | Observational cohort | n = 22 |
| Meditation experience is associated with increased cortical thickness Participants with extensive Insight meditation experience and matched controls | 2005 | Observational cohort | n = 20 |
| Meditation experience is associated with differences in default mode network activity and connectivity Experienced meditators and matched meditation-naive controls | 2011 | Observational cohort | |
| Long-term meditators self-induce high-amplitude gamma synchrony during mental practice Long-term Buddhist practitioners and controls | 2004 | Observational cohort |
Citations in the library
Cites 29
- How Does Mindfulness Meditation Work? Proposing Mechanisms of Action From a Conceptual and Neural Perspective Perspectives on Psychological Science October 14, 2011
- Construct Validity of the Five Facet Mindfulness Questionnaire in Meditating and Nonmeditating Samples Assessment February 29, 2008
- Meditation experience is associated with increased cortical thickness Neuroreport November 7, 2005
- Mindfulness Meditation and Improvement in Sleep Quality and Daytime Impairment Among Older Adults With Sleep Disturbances JAMA Internal Medicine February 16, 2015
- Effects of mindful-attention and compassion meditation training on amygdala response to emotional stimuli in an ordinary, non-meditative state Frontiers in Human Neuroscience 2012
and 24 more in the library