Li Wenbin, Cheng Shan, Dai Jing, Chang Yaoming
Department of Aerospace Hygiene, Faculty of Aerospace Medicine, Air Force Medical University, Xi'an, China.
Department of Aerospace Medical Equipment, Faculty of Aerospace Medicine, Air Force Medical University, Xi'an, China.
Brain Behav. 2025 Jan;15(1):e70216. doi: 10.1002/brb3.70216.
Multitasking during flights leads to a high mental workload, which is detrimental for maintaining task performance. Electroencephalography (EEG) power spectral analysis based on frequency-band oscillations and microstate analysis based on global brain network activation can be used to evaluate mental workload. This study explored the effects of a high mental workload during simulated flight multitasking on EEG frequency-band power and microstate parameters.
Thirty-six participants performed multitasking with low and high mental workloads after 4 consecutive days of training. Two levels of mental workload were set by varying the number of subtasks. EEG signals were acquired during the task. Power spectral and microstate analyses were performed on the EEG. The indices of four frequency bands (delta, theta, alpha, and beta) and four microstate classes (A-D) were calculated, changes in the frequency-band power and microstate parameters under different mental workloads were compared, and the relationships between the two types of EEG indices were analyzed.
The theta-, alpha-, and beta-band powers were higher under the high than under the low mental workload condition. Compared with the low mental workload condition, the high mental workload condition had a lower global explained variance and time parameters of microstate B but higher time parameters of microstate D. Less frequent transitions between microstates A and B and more frequent transitions between microstates C and D were observed during high mental workload conditions. The time parameters of microstate B were positively correlated with the delta-, theta-, and beta-band powers, whereas the duration of microstate C was negatively correlated with the beta-band power.
EEG frequency-band power and microstate parameters can be used to detect a high mental workload. Power spectral analyses based on frequency-band oscillations and microstate analyses based on global brain network activation were not completely isolated during multitasking.
飞行过程中的多任务处理会导致较高的心理负荷，这对维持任务表现不利。基于频段振荡的脑电图（EEG）功率谱分析以及基于全脑网络激活的微状态分析可用于评估心理负荷。本研究探讨了模拟飞行多任务处理期间高心理负荷对EEG频段功率和微状态参数的影响。
36名参与者在连续4天的训练后进行了低心理负荷和高心理负荷的多任务处理。通过改变子任务的数量设置了两个心理负荷水平。在任务执行期间采集EEG信号。对EEG进行功率谱和微状态分析。计算了四个频段（δ、θ、α和β）的指标以及四个微状态类别（A - D）的指标，比较了不同心理负荷下频段功率和微状态参数的变化，并分析了两种EEG指标之间的关系。
高心理负荷条件下的θ、α和β频段功率高于低心理负荷条件。与低心理负荷条件相比，高心理负荷条件下微状态B的全局解释方差和时间参数较低，但微状态D的时间参数较高。在高心理负荷条件下，观察到微状态A和B之间的转换频率较低，而微状态C和D之间的转换频率较高。微状态B的时间参数与δ、θ和β频段功率呈正相关，而微状态C的持续时间与β频段功率呈负相关。
EEG频段功率和微状态参数可用于检测高心理负荷。基于频段振荡的功率谱分析和基于全脑网络激活的微状态分析在多任务处理过程中并非完全独立。
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