Unilateral spatial neglect (USN) is a complex spatial attentional disorder consisting of a failure to attend to the contralesional side of space, frequently seen after a stroke. However, the majority of cases go undiagnosed due to the lack of a valid and reliable tool that is able to assess USN and its many variants. Recent technological advances in virtual reality (VR) and physiological sensors, allow for the study of this disorder under controlled, and ecologically-valid environments, which hold the promise of reliable and early detection. This proof of concept study aims to evaluate the feasibility of a system for discriminating different attentional states using a multimodal dataset derived from a spatial attention task conducted in VR. Nine healthy young adults underwent two experimental conditions: a Control condition and a Left Occlusion condition. Participants performed a visual search task while their behavioral data, including performance metrics, eye-gaze, head, and controller movement data, were recorded. Additionally, electroencephalography data was synchroniously collected to capture neural correlates of attentional processing. Analysis of results of this within-subjects study found worse performance (higher RT), changes in behavior (right-ward gaze bias, left-ward bias in head and controller movement) in the Left Occlusion condition. Neural differences were found (parieto-occipital mean alpha band power and event related potentials) between the two conditions. If validated, this system could be utilized as a diagnostic VR tool, while it holds the potential to facilitate the participation of stroke patients with USN in VR-driven rehabilitation.
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