Research on the influence of mental fatigue on information resources allocation of working memory_Journal of Biomedical Engineering

Authors：

 YANG Shuo ^1,2 , WANG Zengxin ^1,2 , WANG Lei ^1,2 , SHI Baixue ^1,2 , PENG Sen ^1,2

1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China;
2. Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, P.R.China;

Corresponding author：

YANG Shuo, Email: sureyang@126.com

Keywords：

mental fatigue; working memory; information resources; brain network

DOI：

10.7507/1001-5515.202007050

Video：

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Mental fatigue is the subjective state of people after excessive consumption of information resources. Its impact on cognitive activities is mainly manifested as decreased alertness, poor memory and inattention, which is highly related to the performance after impaired working memory. In this paper, the partial directional coherence method was used to calculate the coherence coefficient of scalp electroencephalogram (EEG) of each electrode. The analysis of brain network and its attribute parameters was used to explore the changes of information resource allocation of working memory under mental fatigue. Mental fatigue was quickly induced by the experimental paradigm of adaptive N-back working memory. Twenty-five healthy college students were randomly recruited as subjects, including 14 males and 11 females, aged from 20 to 27 years old, all right-handed. The behavioral data and resting scalp EEG data were collected simultaneously. The results showed that the main information transmission pathway of the brain changed under mental fatigue, mainly in the frontal lobe and parietal lobe. The significant changes in brain network parameters indicated that the information transmission path of the brain decreased and the efficiency of information transmission decreased significantly. In the causal flow of each electrode and the information flow of each brain region, the inflow of information resources in the frontal lobe decreased under mental fatigue. Although the parietal lobe region and occipital lobe region became the main functional connection areas in the fatigue state, the inflow of information resources in these two regions was still reduced as a whole. These results indicated that mental fatigue affected the information resources allocation of working memory, especially in the frontal and parietal regions which were closely related to working memory.

Citation： YANG Shuo, WANG Zengxin, WANG Lei, SHI Baixue, PENG Sen. Research on the influence of mental fatigue on information resources allocation of working memory. Journal of Biomedical Engineering, 2021, 38(4): 671-677. doi: 10.7507/1001-5515.202007050 Copy

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5.	李颖洁, 李玉玲, 杨帮华. 基于脑电信号深度学习的情绪识别研究现状. 北京生物医学工程, 2020, 39(6): 634-642.
6.	尚婧. 基于EEG数据的脑网络构建及分析方法研究. 西安: 西安电子科技大学, 2017.
7.	Yin Z, Li J, Zhang Y, et al. Functional brain network analysis of schizophrenic patients with positive and negative syndrome based on mutual information of EEG time series. Biomed Signal Process Control, 2017, 31: 331-338.
8.	杨硕, 李润泽, 丁建清, 等. 基于EMD去趋势波动的脑疲劳模糊熵分析. 中国生物医学工程学报, 2020, 39(1): 33-39.
9.	Li Junhua, Lim Julian, Chen Yu, et al. Mid-task break improves global integration of functional connectivity in lower alpha band. Front Hum Neurosci, 2016, 10: 304.
10.	郝鹏茹. 基于脑电频谱特征的脑疲劳监测分析. 天津: 河北工业大学, 2017.
11.	万育辰. 脑疲劳对注意能力影响的ERP研究. 西安: 第四军医大学, 2011.
12.	边琰, 赵丽, 傅星, 等. 不同视觉辅助刺激下运动想象大脑皮层因果连接网络特性分析. 中国生物医学工程学报, 2020, 39(6): 685-692.
13.	杨硕, 冀亚坤, 李润泽, 等. 基于theta-gamma相位幅值耦合的脑疲劳信息传递整合机制研究. 生物医学工程学杂志, 2018, 35(5): 14-20.
14.	孟桂芳, 许敏鹏, 张春翠, 等. 一种与N-Back诱发脑力疲劳相关的特异性脑电网络结构. 中国生物医学工程学报, 2017, 36(2): 143-149.
15.	Qi Peng, Ru Hua, Gao Lingyun, et al. Neural mechanisms of mental fatigue revisited: new insights from the brain connectome. Engineering, 2019, 5(2): 276-286.
16.	李颖, 张东颖, 苏晴, 等. 基于脑网络的古典音乐和摇滚音乐对工作记忆的影响研究. 中国生物医学工程学报, 2019, 38(2): 129-137.
17.	杨硕, 艾娜, 王磊, 等. 脑疲劳状态的脑功能网络特征分类研究. 生物医学工程学杂志, 2018, 35(2): 171-175.
18.	谢慧, 罗跃嘉, 张丹丹. 基于N-back任务的抑郁群体工作记忆更新研究进展和展望. 心理学通讯, 2019, 2(1): 43-49.
19.	Xia Mingrui, Wang Jinhui, He Yong, et al. BrainNet Viewer: A network visualization tool for human brain connectomics. PLoS One, 2013, 8(7): e68910.
20.	Lehrer J. Neuroscience: Making connections. Nature, 2009, 457(7229): 524-527.
21.	张天恒, 王磊, 郭苗苗, 等. 虚拟现实视觉体验对脑功能网络的影响. 生物医学工程学杂志, 2020, 37(2): 251-261.
22.	沈潇童, 王玥, 毕卉, 等. 基于脑网络参数优化的青少年抑郁症患者与健康人群分类识别研究. 生物医学工程学杂志, 2020, 37(6): 1037-1044, 1055.
23.	王润泽. 基于格兰杰因果的精神分裂症患者因效性脑网络分析. 太原: 太原理工大学, 2019.
24.	Brázdil M, Babiloni C, Roman R, et al. Directional functional coupling of cerebral rhythms between anterior cingulate and dorsolateral prefrontal areas during rare stimuli: a directed transfer function analysis of human depth EEG signal. Hum Brain Mapp, 2009, 30(1): 138-146.
25.	柯丽萍. 体感电刺激时脑电活动的网络特征分析. 北京: 北京协和医学院, 2018.
26.	胡月静, 张启忠, 席旭刚, 等. 基于部分定向相干法的脑网络特征分析. 航天医学与医学工程, 2019, 32(1): 64-71.
27.	Zhang D, Zhao H, Bai W, et al. Functional connectivity among multi-channel EEGs when working memory load reaches the capacity. Brain Res, 2016, 1631: 101-112.
28.	昌凡, 李豪喆, 张盛宇, 等. 基于fNIRS技术检测脑外伤所致轻度认知功能障碍者工作记忆. 法医学杂志, 2020, 36(1): 52-60.
29.	王帅帅, 李颖, 李继鹏, 等. 基于皮层脑网络的背景音乐对空间认知工作记忆影响的实验研究. 生物医学工程学杂志, 2020, 37(4): 587-595.
30.	王程, 朱明星, 汪鑫, 等. 基于脑电波时频分析方法的认知决策任务研究. 集成技术, 2020, 9(4): 11-24.
31.	张瑶. 空间工作记忆的大脑结构基础及其影响因素. 重庆: 西南大学, 2019.
32.	李继鹏, 李颖, 张东颖, 等. 基于脑电信号溯源分析的音乐类型对学习记忆影响的研究. 中国生物医学工程学报, 2019, 38(6): 679-686.
33.	Goulden N, Khusnulina A, Davis N J, et al. The salience network is responsible for switching between the default mode network and the central executive network: Replication from DCM. Neuroimage, 2014, 99: 180-190.

1. 徐明伟, 金龙哲, 张安琪. 基于EEG的有限空间检修工脑疲劳特性研究. 中国安全生产科学技术, 2020, 16(6): 171-176.
2. Ericsson K A, Kintsch W. Long-term working memory. Psychol Rev, 1995, 102(2): 211-245.
3. 连重源. 基于脑电分析的轻度认知障碍语言功能网络损伤研究. 深圳: 中国科学院大学 (中国科学院深圳先进技术研究院), 2020.
4. Baddeley A. Working memory and emotion: ruminations on a theory of depression. Rev Gen Psychol, 2013, 17(1): 20-27.
5. 李颖洁, 李玉玲, 杨帮华. 基于脑电信号深度学习的情绪识别研究现状. 北京生物医学工程, 2020, 39(6): 634-642.
6. 尚婧. 基于EEG数据的脑网络构建及分析方法研究. 西安: 西安电子科技大学, 2017.
7. Yin Z, Li J, Zhang Y, et al. Functional brain network analysis of schizophrenic patients with positive and negative syndrome based on mutual information of EEG time series. Biomed Signal Process Control, 2017, 31: 331-338.
8. 杨硕, 李润泽, 丁建清, 等. 基于EMD去趋势波动的脑疲劳模糊熵分析. 中国生物医学工程学报, 2020, 39(1): 33-39.
9. Li Junhua, Lim Julian, Chen Yu, et al. Mid-task break improves global integration of functional connectivity in lower alpha band. Front Hum Neurosci, 2016, 10: 304.
10. 郝鹏茹. 基于脑电频谱特征的脑疲劳监测分析. 天津: 河北工业大学, 2017.
11. 万育辰. 脑疲劳对注意能力影响的ERP研究. 西安: 第四军医大学, 2011.
12. 边琰, 赵丽, 傅星, 等. 不同视觉辅助刺激下运动想象大脑皮层因果连接网络特性分析. 中国生物医学工程学报, 2020, 39(6): 685-692.
13. 杨硕, 冀亚坤, 李润泽, 等. 基于theta-gamma相位幅值耦合的脑疲劳信息传递整合机制研究. 生物医学工程学杂志, 2018, 35(5): 14-20.
14. 孟桂芳, 许敏鹏, 张春翠, 等. 一种与N-Back诱发脑力疲劳相关的特异性脑电网络结构. 中国生物医学工程学报, 2017, 36(2): 143-149.
15. Qi Peng, Ru Hua, Gao Lingyun, et al. Neural mechanisms of mental fatigue revisited: new insights from the brain connectome. Engineering, 2019, 5(2): 276-286.
16. 李颖, 张东颖, 苏晴, 等. 基于脑网络的古典音乐和摇滚音乐对工作记忆的影响研究. 中国生物医学工程学报, 2019, 38(2): 129-137.
17. 杨硕, 艾娜, 王磊, 等. 脑疲劳状态的脑功能网络特征分类研究. 生物医学工程学杂志, 2018, 35(2): 171-175.
18. 谢慧, 罗跃嘉, 张丹丹. 基于N-back任务的抑郁群体工作记忆更新研究进展和展望. 心理学通讯, 2019, 2(1): 43-49.
19. Xia Mingrui, Wang Jinhui, He Yong, et al. BrainNet Viewer: A network visualization tool for human brain connectomics. PLoS One, 2013, 8(7): e68910.
20. Lehrer J. Neuroscience: Making connections. Nature, 2009, 457(7229): 524-527.
21. 张天恒, 王磊, 郭苗苗, 等. 虚拟现实视觉体验对脑功能网络的影响. 生物医学工程学杂志, 2020, 37(2): 251-261.
22. 沈潇童, 王玥, 毕卉, 等. 基于脑网络参数优化的青少年抑郁症患者与健康人群分类识别研究. 生物医学工程学杂志, 2020, 37(6): 1037-1044, 1055.
23. 王润泽. 基于格兰杰因果的精神分裂症患者因效性脑网络分析. 太原: 太原理工大学, 2019.
24. Brázdil M, Babiloni C, Roman R, et al. Directional functional coupling of cerebral rhythms between anterior cingulate and dorsolateral prefrontal areas during rare stimuli: a directed transfer function analysis of human depth EEG signal. Hum Brain Mapp, 2009, 30(1): 138-146.
25. 柯丽萍. 体感电刺激时脑电活动的网络特征分析. 北京: 北京协和医学院, 2018.
26. 胡月静, 张启忠, 席旭刚, 等. 基于部分定向相干法的脑网络特征分析. 航天医学与医学工程, 2019, 32(1): 64-71.
27. Zhang D, Zhao H, Bai W, et al. Functional connectivity among multi-channel EEGs when working memory load reaches the capacity. Brain Res, 2016, 1631: 101-112.
28. 昌凡, 李豪喆, 张盛宇, 等. 基于fNIRS技术检测脑外伤所致轻度认知功能障碍者工作记忆. 法医学杂志, 2020, 36(1): 52-60.
29. 王帅帅, 李颖, 李继鹏, 等. 基于皮层脑网络的背景音乐对空间认知工作记忆影响的实验研究. 生物医学工程学杂志, 2020, 37(4): 587-595.
30. 王程, 朱明星, 汪鑫, 等. 基于脑电波时频分析方法的认知决策任务研究. 集成技术, 2020, 9(4): 11-24.
31. 张瑶. 空间工作记忆的大脑结构基础及其影响因素. 重庆: 西南大学, 2019.
32. 李继鹏, 李颖, 张东颖, 等. 基于脑电信号溯源分析的音乐类型对学习记忆影响的研究. 中国生物医学工程学报, 2019, 38(6): 679-686.
33. Goulden N, Khusnulina A, Davis N J, et al. The salience network is responsible for switching between the default mode network and the central executive network: Replication from DCM. Neuroimage, 2014, 99: 180-190.

Journal of Biomedical Engineering

Research on the influence of mental fatigue on information resources allocation of working memory

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