Effects of fatigue on operator monitoring behavior based on EEG experiments

doi:10.16265/j.cnki.issn1003-3033.2024.09.0997

Abstract

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

CLC Number:

X945

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