Hsu Ai-Ling, Wu Chun-Yu, Ng Hei-Yin Hydra, Chuang Chun-Hsiang, Huang Chih-Mao, Wu Changwei W, Chao Yi-Ping
Department of Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.
Comput Methods Programs Biomed. 2024 Dec;257:108446. doi: 10.1016/j.cmpb.2024.108446. Epub 2024 Sep 28.
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.
练习正念是一种朝向内感受性觉知的心理过程，通过脑功能改变实现减压和情绪调节。文献表明，脑电图（EEG）衍生的连通性具有区分未接触过正念和有正念经验者脑功能的潜力，这种定量区分有助于心理健康的远程诊断。然而，在针对正念经验预测的模型选择方面尚无先前的指导。在此，我们假设EEG有效连通性在具有脑可解释性的正念经验中能达到良好的预测性能。
我们旨在探究直接定向传递函数（dDTF）以对参与者基于正念的减压（MBSR）历史进行分类，并通过比较多种机器学习（ML）算法来优化预测准确性。针对γ波段有效连通性，我们评估了基于EEG对7种机器学习（ML）算法和3个时段（即静息、专注呼吸和全身扫描）的正念经验进行的预测。
支持向量机和朴素贝叶斯分类器在所有三个时段均表现出显著高于机遇水平的准确率，与其他两种正念状态的分类准确率相比，决策树算法在静息状态下达到了最高预测准确率91.7%。我们进一步对关键EEG通道进行分析以保持分类准确率，结果表明在19个通道中仅保留4个通道（F7、F8、T7和P7）可产生83.3%的准确率。深入研究连通性特征的贡献，主要位于额叶的特定连通性特征对分类器构建的贡献更大，这与现有的正念文献高度一致。
在本研究中，我们开创了开发基于EEG的分类器以客观检测个体正念经验的里程碑。决策树的预测准确率在使用局部静息态EEG数据区分正念经验方面是最优的。所建议的算法和正念经验预测的关键通道可能为在未来可穿戴神经反馈系统或合理的数字疗法中嵌入基于EEG的分类来预测正念经验提供指导。
