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• W2986017006 abstract "With the recent surge of affordable, high-performance virtual reality (VR) headsets, there is unlimited potential for applications ranging from education, to training, to entertainment, to fitness and beyond. As these interfaces continue to evolve, passive user-state monitoring can play a key role in expanding the immersive VR experience, and tracking activity for user well-being. By recording physiological signals such as the electroencephalogram (EEG) during use of a VR device, the user's interactions in the virtual environment could be adapted in real-time based on the user's cognitive state. Current VR headsets provide a logical, convenient, and unobtrusive framework for mounting EEG sensors. The present study evaluates the feasibility of passively monitoring cognitive workload via EEG while performing a classical n-back task in an interactive VR environment. Data were collected from 15 participants and the spatio-spectral EEG features were analyzed with respect to task performance. The results indicate that scalp measurements of electrical activity can effectively discriminate three workload levels, even after suppression of a co-varying high-frequency activity." @default.
• W2986017006 created "2019-11-22" @default.
• W2986017006 creator A5009915375 @default.
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• W2986017006 creator A5087640289 @default.
• W2986017006 date "2019-11-14" @default.
• W2986017006 modified "2023-10-01" @default.
• W2986017006 title "Estimating Cognitive Workload in an Interactive Virtual Reality Environment Using EEG" @default.
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• W2986017006 doi "https://doi.org/10.3389/fnhum.2019.00401" @default.
• W2986017006 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6868478" @default.
• W2986017006 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31803035" @default.
• W2986017006 hasPublicationYear "2019" @default.
• W2986017006 type Work @default.
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