Graph theoretical analysis of EEG functional network during multi-workload flight simulation experiment in virtual...

Area of Science:

• Neuroscience
• Human-Computer Interaction
• Cognitive Psychology

Background:

• Mental workload is critical for performance and safety.
• Previous research on neural correlates of mental workload primarily used 2D environments and univariate EEG analysis.
• The impact of 3D environments and multivariate analysis on mental workload remains underexplored.

Purpose of the Study:

• To investigate the neural correlates of mental workload in 3D Virtual Reality (VR) versus 2D Computer Screen (CS) environments.
• To apply multivariate graph theory analysis to Electroencephalogram (EEG) data.
• To compare brain connectivity patterns between VR and CS flight simulations.

Main Methods:

• Twenty subjects participated in flight simulations in both CS and VR environments.
• Electroencephalogram (EEG) data were preprocessed and used to construct connectivity matrices via the Phase Lag Index (PLI).
• Graph theory metrics (global efficiency, local efficiency, nodal efficiency) were analyzed in alpha and theta bands.

Main Results:

• Global and local efficiency were generally lower in the VR environment compared to CS across both EEG bands.
• Nodal efficiency showed significant decreases in distinct brain regions for CS and VR conditions.
• The findings indicate differential effects of 2D and 3D environments on brain network organization.

Conclusions:

• Virtual Reality flight simulation appears to induce a higher mental workload than traditional 2D Computer Screen simulation.
• The neural mechanisms underlying mental workload differ significantly between 2D and 3D simulated environments.
• Multivariate graph theory analysis provides novel insights into the brain's response to varying levels of immersion and complexity.