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

Area of Science:

• Neuroscience
• Cognitive Science
• Human-Machine Systems

Background:

• Accurate mental workload evaluation is crucial for human-machine system safety and task performance.
• Current cross-task evaluation using physiological metrics is insufficient.
• Understanding dynamic functional connectivity changes with mental workload is needed.

Purpose of the Study:

• To explore dynamic functional connectivity alterations during varying mental workload across different tasks.
• To investigate the utility of EEG microstate analysis for cross-task mental workload assessment.
• To identify reliable neural markers for workload evaluation.

Main Methods:

• Designed four mental workload tasks with diverse information types.
• Applied a novel dynamic brain network analysis method based on EEG microstates.
• Analyzed six microstate topographies (A-F) and their dynamic functional connectivity.

Main Results:

• Identified 15 nodes and 68 connectivity pairs in the Frontal-Parietal region sensitive to mental workload across all tasks.
• Observed decreased characteristic path length and increased global efficiency in Microstate D networks (Theta and Alpha bands) with higher workload.
• Achieved 95.8% within-task and 80.3% cross-task accuracy using SVM for mental workload discrimination.

Conclusions:

• Dynamic functional connectivity metrics within specific EEG microstates can effectively evaluate cross-task mental workload.
• This approach offers new insights into the neural mechanisms of mental workload across different information types.
• The findings support the feasibility of using EEG microstate-based dynamic brain networks for real-world applications.