EEG Based Dynamic Functional Connectivity Analysis in Mental Workload Tasks With Different Types of Information

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 30; pp. 632 - 642
• Main Authors: Guan, Kai, Zhang, Zhimin, Chai, Xiaoke, Tian, Zhikang, Liu, Tao, Niu, Haijun
• Format: Journal Article
• Language: English
• Published: United States IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain; Brain - physiology; Clustering algorithms; Cognitive ability; Connectivity analysis; different information types; dynamic functional connectivity; EEG; Electroencephalography; Electroencephalography - methods; Humans; Man machine systems; Measurement; Mental workload; microstate analysis; Network analysis; Neural networks; Operators; Parietal Lobe; Physiology; Support vector machines; Surfaces; Task analysis; Vehicle dynamics; Workload; Workload - psychology; Workloads
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2022.3156546

The accurate evaluation of operators' mental workload in human-machine systems plays an important role in ensuring the correct execution of tasks and the safety of operators. However, the performance of cross-task mental workload evaluation based on physiological metrics remains unsatisfactory. To explore the changes in dynamic functional connectivity properties with varying mental workload in different tasks, four mental workload tasks with different types of information were designed and a newly proposed dynamic brain network analysis method based on EEG microstate was applied in this paper. Six microstate topographies labeled as Microstate A-F were obtained to describe the task-state EEG dynamics, which was highly consistent with previous studies. Dynamic brain network analysis revealed that 15 nodes and 68 pairs of connectivity from the Frontal-Parietal region were sensitive to mental workload in all four tasks, indicating that these nodal metrics had potential to effectively evaluate mental workload in the cross-task scenario. The characteristic path length of Microstate D brain network in both Theta and Alpha bands decreased whereas the global efficiency increased significantly when the mental workload became higher, suggesting that the cognitive control network of brain tended to have higher function integration property under high mental workload state. Furthermore, by using a SVM classifier, an averaged classification accuracy of 95.8% for within-task and 80.3% for cross-task mental workload discrimination were achieved. Results implies that it is feasible to evaluate the cross-task mental workload using the dynamic functional connectivity metrics under specific microstate, which provided a new insight for understanding the neural mechanism of mental workload with different types of information.