Investigating cognitive workload in virtual reality motor interactions: a neuroergonomic approach using EEG

Abstract

Virtual reality (VR) environments rely heavily on motor interaction techniques that can shape users’ cognitive workload and overall experience. This study examined three interaction methods Near, Ray, and GoGo select to compare their impacts on cognitive workload using electroencephalographic (EEG) measures and NASA-TLX ratings. Twenty healthy participants completed VR motor tasks while their EEG was recorded, and each participant performed all three interaction methods. Spectral EEG analysis showed mixed results: beta power differed significantly across techniques, theta power did not, and alpha power showed an omnibus effect without significant corrected pairwise differences. In contrast, the EEG workload index based on the frontal theta/parietal alpha ratio (Index 1) showed a significant task effect, with Ray select exhibiting the highest workload. Ray select also received higher NASA-TLX ratings for mental, physical, and temporal demands, as well as effort and frustration. Repeated-measures correlations indicated that Index 3 was significantly related to several NASA-TLX subscales, whereas Index 1 and Index 2 were not, suggesting that the EEG indices capture different aspects of workload rather than a single unified construct. Near select consistently showed the lowest workload, while GoGo select produced intermediate scores. These findings suggest that ray-based object selection requires greater cognitive workload, likely because it combines visuomotor precision with multitasking demands. Overall, the results indicate that EEG composite indices may be more sensitive than raw band-power measures for distinguishing workload differences across VR interaction techniques, and they support the use of neuroergonomic measures for guiding VR interface design.