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在年轻成年人中,对单个简短的集中正念冥想进行脑电图神经相关和深度学习分析:试点研究

Yongxin Luo^1,2, Yaoyao Zhang^3,4, Yuqing Zhai^1,2

¹School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

|October 28, 2025

概括

一次简短的专注正念冥想 (BFMM) 会话显著降低了年轻成年人的心率和呼吸率. 深度学习模型在BFMM期间有效地分类电脑电图 (EEG) 信号,表明其应激管理潜力.

科学领域:

神经科学是一个神经科学.
心理生理学心理生理学
计算精神病学是一种计算精神病学.

背景情况:

简短的专注正念冥想 (BFMM) 是一种用于减轻压力的技术.
了解BFMM的生理和神经相关性对于其应用至关重要.
脑电图 (EEG) 提供了对冥想期间大脑活动的洞察.

研究的目的:

调查BFMM与年轻成年人的生理/神经压力标志物之间的关联.
在BFMM期间使用深度学习模型对EEG信号进行分类.
确定最有效的深度学习模型,以区分BFMM和静止状态.

主要方法:

一个前后研究设计,有24名年轻成年参与者.
在BFMM前后测量生理指数 (心率,呼吸率).
在休息和BFMM状态下采集EEG信号,使用MLP,LSTM,CNN和集体深度学习模型进行分析.

主要成果:

在BFMM后,心率 (p < 0.001) 和呼吸率 (p < 0.001) 显著降低.
与休息相比,在BFMM期间在特定大脑区域的theta和beta频段中明显的EEG功率光谱密度差异.
一个集体深度学习模型 (MLP+LSTM+CNN) 在分类EEG信号时实现了79.0%的准确性.

结论:

一次BFMM会话可以有效调节自主神经系统并调节神经活动.
开发的整体模型显示了使用EEG对正念冥想进行客观评估的潜力.
研究结果支持使用BFMM作为压力管理工具,并为未来基于EEG的正念研究提供基础.

关键词:

电脑电图 (电脑电图) 是一种脑电图.深度学习是一种深度学习.组合学习组合学习正念冥想冥想冥想年轻的成年人年轻的成年人

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