Fatigue Characterization of EEG Brain Networks Under Mixed Reality Stereo Vision

Area of Science:

• Neuroscience
• Human-Computer Interaction
• Cognitive Science

Background:

• Mixed Reality (MR) technology offers significant potential across various sectors like industry, healthcare, and education.
• Prolonged use of MR devices for stereoscopic content can induce visual fatigue.
• Visual fatigue affects multiple brain regions, necessitating investigation into associated neural network changes.

Purpose of the Study:

• To explore the topological characteristics of brain networks during visual fatigue induced by MR device usage.
• To analyze changes in brain connectivity and network properties using electroencephalogram (EEG) data.
• To identify specific brain regions and network alterations associated with visual fatigue.

Main Methods:

• Electroencephalogram (EEG) data were collected from participants in both comfort and fatigue states.
• Phase-Locked Value (PLV) was calculated to measure phase synchronization between all channel pairs.
• Sparse brain networks were constructed using PLV, and node/edge properties (betweenness centrality, clustering coefficient, node efficiency, characteristic path length) were analyzed across frequency bands.

Main Results:

• A notable enhancement in brain connectivity was observed in the alpha, theta, and delta frequency bands during the fatigue state.
• Mean values of network properties (betweenness centrality, clustering coefficient, nodal efficiency) were higher in the fatigue state compared to the comfort state.
• Significant alterations in network properties were identified in frontal, parietal, temporal, and central brain regions, with increased long-distance connections observed during fatigue.

Conclusions:

• Prolonged MR device use leading to visual fatigue is associated with significant alterations in brain network topology and connectivity.
• Increased brain connectivity and changes in network properties, particularly in frontal and parietal regions, are key indicators of visual fatigue.
• Understanding these neural mechanisms can inform strategies to mitigate visual fatigue in high-demand cognitive fields utilizing MR technology.