Attar Eyad Talal
Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia.
Front Hum Neurosci. 2025 Aug 26;19:1616456. doi: 10.3389/fnhum.2025.1616456. eCollection 2025.
This scientific investigation explored how meditation influences neural sound stimulus responses by employing EEG techniques during both meditative states and auditory oddball tasks. The study evaluated event-related potentials alongside theta, alpha and beta spectral power while employing machine learning techniques to distinguish meditative states from cognitive tasks.
The study utilized data from 13 participants aged 24-58, which researchers obtained through an openly accessible OpenNeuro dataset.
Examination of eventrelated potentials (ERPs) demonstrated that P300 amplitude showed significant growth when responding to oddball stimuli, which indicates increased attention allocation ( < 0.05). Spectral power analysis demonstrated an increase in frontal alpha and beta power during meditation while central theta power decreased, which suggests reduced cognitive load and enhanced internal focus. Meditation experience showed a statistical relationship with frontal alpha power, where = 0.45 and < 0.03. A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.
The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain-computer interfaces and mental health applications. The study of meditation's effects on brain activity reveals its benefits for emotional regulation and concentration improvement. The research findings deliver strong evidence that meditation induces distinct neural modifications detectable through ERP and spectral analysis. The potential for meditation to enhance cortical efficiency alongside emotion self-regulation indicates its viability as a mental health support tool. The integration of EEG biomarkers with machine learning methods emerges as a potential pathway for real-time cognitive and emotional state monitoring which enables tailored interventions through neurofeedback systems and brain-computer interfaces to boost cognitive function and emotional health across clinical settings and everyday life.
本科学研究通过在冥想状态和听觉oddball任务期间采用脑电图技术，探索了冥想如何影响神经声音刺激反应。该研究在利用机器学习技术区分冥想状态和认知任务的同时，评估了事件相关电位以及theta、alpha和beta频谱功率。
该研究使用了来自13名年龄在24 - 58岁参与者的数据，研究人员通过一个可公开获取的OpenNeuro数据集获得这些数据。
对事件相关电位（ERP）的检查表明，在对oddball刺激做出反应时，P300波幅显著增加，这表明注意力分配增加（<0.05）。频谱功率分析表明，冥想期间额叶alpha和beta功率增加，而中央theta功率降低，这表明认知负荷降低且内在注意力增强。冥想经验与额叶alpha功率存在统计学关系，其中=0.45且<0.03。随机森林分类器的准确率达到86%。该系统在区分认知状态和冥想状态时，以P300波幅、额叶alpha功率以及beta功率作为显著预测指标，准确率达到7%。
基于脑电图的神经反馈系统在用于医疗保健脑机接口和心理健康应用的实时认知状态检测方面显示出潜力。对冥想对大脑活动影响的研究揭示了其对情绪调节和注意力改善的益处。研究结果提供了强有力的证据，表明冥想会引起可通过ERP和频谱分析检测到的独特神经变化。冥想在提高皮层效率以及情绪自我调节方面的潜力表明其作为心理健康支持工具的可行性。脑电图生物标志物与机器学习方法的整合成为实时认知和情绪状态监测的潜在途径，这使得能够通过神经反馈系统和脑机接口进行定制干预，以在临床环境和日常生活中提高认知功能和情绪健康。
