Academy of Medical Engineering and Translational Medicine, Tianjin International Joint Research Centre for Neural Engineering, and Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, People's Republic of China.
Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, People's Republic of China.
Hum Brain Mapp. 2024 Jan;45(1):e26552. doi: 10.1002/hbm.26552. Epub 2023 Dec 5.
Electroencephalography (EEG) microstate analysis has become a popular tool for studying the spatial and temporal dynamics of large-scale electrophysiological activities in the brain in recent years. Four canonical topographies of the electric field (classes A, B, C, and D) have been widely identified, and changes in microstate parameters are associated with several psychiatric disorders and cognitive functions. Recent studies have reported the modulation of EEG microstate by mental workload (MWL). However, the common practice of evaluating MWL is in a specific task. Whether the modulation of microstate by MWL is consistent across different types of tasks is still not clear. Here, we studied the topographies and dynamics of microstate in two independent MWL tasks: NBack and the multi-attribute task battery (MATB) and showed that the modulation of MWL on microstate topographies and parameters depended on tasks. We found that the parameters of microstates A and C, and the topographies of microstates A, B, and D were significantly different between the two tasks. Meanwhile, all four microstate topographies and parameters of microstates A and C were different during the NBack task, but no significant difference was found during the MATB task. Furthermore, we employed a support vector machine recursive feature elimination procedure to investigate whether microstate parameters were suitable for MWL classification. An averaged classification accuracy of 87% for within-task and 78% for cross-task MWL discrimination was achieved with at least 10 features. Collectively, our findings suggest that topographies and parameters of microstates can provide valuable information about neural activity patterns with a dynamic temporal structure at different levels of MWL, but the modulation of MWL depends on tasks and their corresponding functional systems. Moreover, as a potential indicator, microstate parameters could be used to distinguish MWL.
脑电图（EEG）微状态分析近年来已成为研究大脑中大规模电生理活动的空间和时间动态的一种流行工具。已广泛识别出电场的四个典型拓扑结构（A、B、C 和 D 类），微状态参数的变化与几种精神障碍和认知功能有关。最近的研究报告了脑电图微状态受心理工作量（MWL）的调制。然而，评估 MWL 的常见做法是在特定任务中。MWL 对微状态的调制是否在不同类型的任务中一致尚不清楚。在这里，我们研究了两个独立的 MWL 任务（NBack 和多属性任务电池（MATB）中的微状态的拓扑结构和动态，并表明 MWL 对微状态拓扑结构和参数的调制取决于任务。我们发现，微状态 A 和 C 的参数以及微状态 A、B 和 D 的拓扑在两个任务之间存在显著差异。同时，在 NBack 任务期间，所有四个微状态拓扑结构和微状态 A 和 C 的参数都不同，但在 MATB 任务期间没有发现显著差异。此外，我们采用支持向量机递归特征消除程序来研究微状态参数是否适合 MWL 分类。在 NBack 任务中，微状态参数的分类准确率平均为 87%，在 MATB 任务中为 78%，至少需要 10 个特征。总的来说，我们的研究结果表明，微状态的拓扑结构和参数可以提供有关不同 MWL 水平下神经活动模式的有价值信息，具有动态的时间结构，但 MWL 的调制取决于任务及其相应的功能系统。此外，作为一种潜在的指标，微状态参数可以用于区分 MWL。
