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短期诵经对脑电图微状态的影响。

Effect of short-term chanting on electroencephalographic microstates.

作者信息

Tayade Prashant, Saini Manorma, Saini Gaurav, Muthukrishnan Suriya Prakash, Kaur Simran, Sharma Ratna, Sahoo Abhisek

机构信息

Stress and Cognitive Electro-Imaging Laboratory, Department of Physiology, All India Institute of Medical Sciences, New Delhi, India.

Department of Physiology, All India Institute of Medical Sciences, Kalyani, West Bengal, India.

出版信息

Pan Afr Med J. 2024 Nov 14;49:76. doi: 10.11604/pamj.2024.49.76.44648. eCollection 2024.

Abstract

INTRODUCTION

chanting in meditation reduces stress and it is reported to have a relaxation effect during both verbal "OM" chanting (VOM) and listening to "OM" chanting (LOM). There is paucity of literature on EEG microstates information after VOM and LOM using qEEG compared to the resting condition.

METHODS

therefore, to examine the effect of these actions on the brain using qEEG, it is required to compare the EEG microstates among the baseline, VOM, and LOM. In the present work, 23 adult male subjects were examined and given a paradigm designed using E-prime for both VOM and LOM chanting each of 5 min duration. A 128-channel geodesic sensor net was used to obtain the experimental data, which was later pre-processed, segmented, and analysed.

RESULTS

the present work is the first to report the three scalp maps topographies, i.e. microstates obtained utilizing k-means cluster analysis for the response of the VOM and LOM. Also, the number of time frames, Global Explained Variance (GEV), time coverage, and mean duration parameters for the three maps were analysed statistically.

CONCLUSION

the study revealed three microstate topographies as markers and reported no significant effect/changes for the short-term chanting.

摘要

引言

冥想中的唱诵可减轻压力,据报道,在口头唱诵“唵”(VOM)和聆听“唵”唱诵(LOM)过程中均有放松效果。与静息状态相比,利用定量脑电图(qEEG)研究VOM和LOM后脑电图微状态信息的文献较少。

方法

因此,为了使用qEEG研究这些行为对大脑的影响,需要比较基线、VOM和LOM之间的脑电图微状态。在本研究中,对23名成年男性受试者进行了检查,并为VOM和LOM设计了一个使用E-prime的范式,每次唱诵持续5分钟。使用128通道测地线传感器网络获取实验数据,随后对其进行预处理、分段和分析。

结果

本研究首次报告了三种头皮图谱地形,即利用k均值聚类分析获得的VOM和LOM反应的微状态。此外,还对这三种图谱的时间帧数、全局解释方差(GEV)、时间覆盖率和平均持续时间参数进行了统计分析。

结论

该研究揭示了三种微状态地形作为标志物,并报告短期唱诵无显著影响/变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7dd/11845998/0496bb926713/PAMJ-49-76-g001.jpg

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