Classification of VR-Gaming Difficulty Induced Stress Levels using Physiological (EEG & ECG) Signals and Machine Learning
2021
"This work has been submitted to the IEEE Transactions on Affective Computing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible."Physiological sensing has long been an indispensable
fixture for virtual reality (VR) gaming studies. Moreover, VR induced stressors are increasingly being used to assess the impact
of stress on an individual’s health and well-being. This study
discusses the results of experimental research comprising multimodal physiological signal acquisition from 31 participants during a Go/No-Go VR-based shooting exercise where participants
had to shoot the enemy and spare the friendly targets. The study
encompasses multiple sessions, including orientation, thresholding, and shooting. The shooting sessions consist of tasks under
low & high difficulty induced stress conditions with in-between
baseline segments. Machine learning (ML) performance with
heart rate variability (HRV) from electrocardiogram (ECG) and
electroencephalogram (EEG) features outperform the prevalent
methods for four different VR gaming difficulty-induced stress
(GDIS) classification problems (CPs). Further, the significance of
the HRV predictors and different brain region activations from
EEG is deciphered using statistical hypothesis testing (SHT). The
ablation study shows the efficacy of multimodal physiological
sensing for different gaming difficulty-induced stress classification
problems (GDISCPs) in a VR shooting task.
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