疲劳对操纵员监视行为影响机制的脑电试验

doi:10.16265/j.cnki.issn1003-3033.2024.09.0997

中国安全科学学报 ›› 2024, Vol. 34 ›› Issue (9): 50-58.doi: 10.16265/j.cnki.issn1003-3033.2024.09.0997

• 安全社会科学与安全管理 • 上一篇下一篇

疲劳对操纵员监视行为影响机制的脑电试验

张勉¹(), 易灿南², 蒋复量¹^,²^,^**(), 胡鸿², 吴文², 张思佳²

¹ 南华大学资源环境与安全工程学院,湖南衡阳 421001
² 湖南工学院安全与管理工程学院,湖南衡阳 421002

收稿日期:2024-03-26 修回日期:2024-06-26 出版日期:2024-09-28
通信作者:
^** 蒋复量(1978—),男,湖南东安人,博士,教授,主要从事安全管理与风险分析等方面的研究。E-mail: jfljfd@163.com。
作者简介:
张勉 (1999—),男,四川广元人,硕士研究生,研究方向为核设施安全监测、预警理论与技术。E-mail:158788876@qq.com。
易灿南，教授；
胡鸿，教授
基金资助:
国家级大学生创新创业训练计划项目(202211528009); 湖南工学院省级应用特色学科安全科学与工程开放基金资助(KFB22028); 湖南工学院省级应用特色学科安全科学与工程开放基金资助(KFB22029); 湖南省教育厅项目(21C0811)

Effects of fatigue on operator monitoring behavior based on EEG experiments

ZHANG Mian¹(), YI Cannan², JIANG Fuliang¹^,²^,^**(), HU Hong², WU Wen², ZHANG Sijia²

¹ School of Resources Environment and Safety Engineering, University of South China, Hengyang Hunan 421001, China
² School of Safety and Management Engineering, Hunan Institute of Technology, Hengyang Hunan 421002, China

Received:2024-03-26 Revised:2024-06-26 Published:2024-09-28

摘要/Abstract

摘要：

为优化操纵员疲劳风险管理,降低疲劳对操纵员作业绩效的影响,完成疲劳诱发前后的监视任务,首先,以某压水堆核电厂数字化主控室为背景,设计2-back范式的疲劳诱发任务和oddball范式的监视任务,并以低、中、高3种复杂度的控制界面为素材;然后,采集23名被试的主观疲劳评分、行为数据和脑电(EEG)信号,用相关疲劳指标验证疲劳诱发的有效性;最后,对比分析疲劳诱发前后3种不同复杂度界面下被试的行为数据、事件相关电位P3成分的差异性。结果表明:最短30 min、最长60 min的2-back任务能够诱发疲劳;疲劳或界面复杂度增加都会导致监视行为的绩效水平下降,疲劳状态下高复杂度界面与中复杂度界面之间的绩效水平差异最大;其中,正确率下降4.9%,反应时增加10.4%;P3潜伏期的变化趋势与反应时一致,P3波幅仅在高复杂度界面下显著增大。结合绩效数据和事件相关电位数据变化特征分析表明:界面复杂度的提高会加深疲劳对操纵员脑力负荷的负面影响。

关键词: 疲劳, 操纵员, 监视行为, 脑电(EEG), 界面复杂度

Abstract:

To optimize operator fatigue risk management and reduce the impact of fatigue on operator performance, the monitoring tasks before and after fatigue induction were conducted. Firstly, the fatigue-inducing task of the 2-back paradigm and the monitoring task of the oddball paradigm were designed with the digital main control room of a pressurized-water reactor nuclear power plant, and the control interfaces of low, medium, and high complexity were used. Then, human trials with 23 participants were performed to obtain subjective fatigue ratings, behavioral data, and EEG signals. Moreover, the effectiveness of fatigue induction was verified using relevant fatigue indicators. Finally, the participator's behavioral data and event-related potential P3 components under three different complexity interfaces before and after fatigue induction were analyzed. The results showed that the shortest 2-back task lasting 30 to 60 min induced fatigue. Fatigue or interface complexity increase resulted in a decrease in the monitoring behavior performance, and the maximum performance difference was observed between high-complexity and medium-complexity interfaces under fatigue conditions. Moreover, a 4.9% decrease in accuracy and a 10.4% increase in reaction time were observed. The trend of P3 latency was consistent with that of reaction time, and P3 amplitude increased significantly only under high-complexity interfaces. Based on the performance analysis and event-related potential data, it was concluded that the interface complexity increased the negative impact of fatigue on operators' mental workload.

Key words: fatigue, operators, monitor behavior, electro-encephalogram(EEG), interface complexity

中图分类号:

X945

张勉, 易灿南, 蒋复量, 胡鸿, 吴文, 张思佳. 疲劳对操纵员监视行为影响机制的脑电试验[J]. 中国安全科学学报, 2024, 34(9): 50-58.

ZHANG Mian, YI Cannan, JIANG Fuliang, HU Hong, WU Wen, ZHANG Sijia. Effects of fatigue on operator monitoring behavior based on EEG experiments[J]. China Safety Science Journal, 2024, 34(9): 50-58.

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分类	H范围	H平均值
低复杂度	(1.112,1.732]	1.274 3
中复杂度	(1.732,2.351]	1.979 5
高复杂度	(2.351,2.969]	2.597 3

任务编号	绩效类型	均值	标准差
B-1	正确率/%	89.0	3.5
B-1	反应时/ms	839.38	113.33
B-2	正确率/%	87.0	5.0
B-2	反应时/ms	922.25	115.13

任务编号	绩效类型	均值	标准差
L-1	正确率/%	96.5	4.1
L-1	反应时/ms	561.58	55.47
M-1	正确率/%	95.1	6.2
M-1	反应时/ms	575.52	62.74
H-1	正确率/%	93.2	5.3
H-1	反应时/ms	621.64	55.88
L-2	正确率/%	94.6	5.7
L-2	反应时/ms	578.62	65.29
M-2	正确率/%	93.2	5.9
M-2	反应时/ms	593.66	62.6
H-2	正确率/%	88.3	9.8
H-2	反应时/ms	655.79	59

数据类型	配对样本	t	p
正确率	L-1&L-2	1.91	0.069
	M-1&M-2	0.84	0.41
	H-1&H-2	3.333	0.003
反应时	L-1&L-2	-1.776	0.09
	M-1&M-2	-3.141	0.044
	H-1&H-2	-5.565	0

数据类型	配对样本	t	p
P3潜伏期	L-1&L-2	0.118	0.907
	M-1&M-2	-1.475	0.154
	H-1&H-2	-3.529	0.002
P3波幅	L-1&L-2	-0.309	0.760
	M-1&M-2	0.564	0.679
	H-1&H-2	-2.234	0.036

疲劳对操纵员监视行为影响机制的脑电试验

Effects of fatigue on operator monitoring behavior based on EEG experiments

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参考文献 22

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指标计算方式	前测	后测	t	p
(α+θ)/β	24.3±9.7	43.5±55.1	-2.223	0.037
(α+θ)/(α+β)	2.7±1.3	3.8±2.4	-2.734	0.012
θ/β	7.8±6.1	21±28.2	-2.385	0.026

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