Key result
Machine learning using heart rate variability from ECG and EEG features outperformed prevalent methods for classifying VR gaming difficulty-induced stress.
Why the study?
VR-induced stressors are increasingly used to evaluate the impact of stress on health and well-being, but effective classification using multimodal physiological sensing is needed.
Multimodal physiological sensing (ECG and EEG) combined with machine learning effectively classifies stress levels induced by varying difficulties in VR gaming.
May aid VR stress research; leaves open clinical translation of ECG-EEG ML models.
“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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Pratiher et al. (2021) studied Stress (n=31). Machine learning with multimodal physiological sensing (EEG & ECG) vs. Prevalent methods was evaluated on Classification of four different VR gaming difficulty-induced stress problems. Machine learning using heart rate variability from ECG and EEG features outperformed prevalent methods for classifying VR gaming difficulty-induced stress.
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