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认知疲劳及其通过虚拟现实干预恢复的神经影像学特征:脑电图微状态分析

Neuroimaging features for cognitive fatigue and its recovery with VR intervention: An EEG microstates analysis.

作者信息

Han Jia-Cheng, Zhang Chi, Cai Yan-Dong, Li Yu-Ting, Shang Yu-Xuan, Chen Zhu-Hong, Yang Guan, Song Jia-Jie, Su Dan, Bai Ke, Sun Jing-Ting, Liu Yu, Liu Na, Duan Ya, Wang Wen

机构信息

Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Xi'an, Shaanxi 710038, China.

School of Aerospace Engineering, Tsinghua University, Beijing 100084, China; Airborne Avionics Flight Test Institute, Chinese Flight Test Establishment, Xi'an, Shaanxi 710089, China.

出版信息

Brain Res Bull. 2025 Feb;221:111223. doi: 10.1016/j.brainresbull.2025.111223. Epub 2025 Jan 24.

Abstract

INTRODUCTION

Cognitive fatigue is mainly caused by enduring mental stress or monotonous work, impairing cognitive and physical performance. Natural scene exposure is a promising intervention for relieving cognitive fatigue, but the efficacy of virtual reality (VR) simulated natural scene exposure is unclear. We aimed to investigate the effect of VR natural scene on cognitive fatigue and further explored its underlying neurophysiological alterations with electroencephalogram (EEG) microstates analysis.

METHODS

Ten participants performed a 20-minute 1-back task before and after VR intervention while EEG was recorded (pre-task, post-task). Performance was measured with mean accuracy rate (MAR) and mean reaction time (MRT) of the continuous 1-back task. VR simulation of the Canal Town scene was utilized to alleviate cognitive fatigue caused by 1-back tasks. Four resting-state phases were identified: beginning, pre, post, and end phases.

RESULTS

Post-task had a higher MAR and a lower MRT than pre-task. For pre-task, MAR was negatively correlated with trials, while MRT was positively correlated with trials. Four EEG microstates classes (A-D) were identified, and their temporal parameters (mean duration, time coverage and occurrence) and transition probabilities were calculated. After intervention, mean duration and time coverage of class B decreased, all parameters of class C increased, while all parameters of class D decreased. Transition probabilities between classes B and D decreased but increased between classes A and C.

CONCLUSION

VR simulation of Canal Town scene is a potentially effective method to alleviate cognitive fatigue. Microstate is an electrophysiological trait characteristic of cognitive fatigue and might be used to indicate the effect of VR intervention.

摘要

引言

认知疲劳主要由长期的精神压力或单调的工作引起,会损害认知和身体表现。自然场景暴露是缓解认知疲劳的一种有前景的干预措施,但虚拟现实(VR)模拟自然场景暴露的效果尚不清楚。我们旨在研究VR自然场景对认知疲劳的影响,并通过脑电图(EEG)微状态分析进一步探索其潜在的神经生理学改变。

方法

10名参与者在VR干预前后进行了20分钟的1-back任务,同时记录脑电图(任务前、任务后)。通过连续1-back任务的平均准确率(MAR)和平均反应时间(MRT)来衡量表现。利用运河镇场景的VR模拟来缓解由1-back任务引起的认知疲劳。确定了四个静息状态阶段:开始、任务前、任务后和结束阶段。

结果

任务后的MAR高于任务前,MRT低于任务前。对于任务前,MAR与试验次数呈负相关,而MRT与试验次数呈正相关。识别出四个EEG微状态类别(A-D),并计算了它们的时间参数(平均持续时间、时间覆盖率和发生率)以及转换概率。干预后,B类的平均持续时间和时间覆盖率降低,C类的所有参数增加,而D类的所有参数降低。B类和D类之间的转换概率降低,但A类和C类之间的转换概率增加。

结论

运河镇场景的VR模拟是缓解认知疲劳的一种潜在有效方法。微状态是认知疲劳的一种电生理特征,可能用于指示VR干预的效果。

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