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Francesco Chiossi, Changkun Ou, Sven Mayer
Optimizing Visual Complexity for Physiologically-Adaptive VR Systems: Evaluating a Multimodal Dataset using EDA, ECG and EEG Features
International Conference on Advanced Visual Interfaces 2024 (AVI'24), Association for Computing Machinery, 2024-06-03 (bib)

Physiologically-adaptive Virtual Reality systems dynamically adjust virtual content based on users' physiological signals to enhance interaction and achieve specific goals. However, as different users' cognitive states may underlie multivariate physiological patterns, adaptive systems necessitate a multimodal evaluation to investigate the relationship between input physiological features and target states for efficient user modeling. Here, we investigated a multimodal dataset (EEG, ECG, and EDA) while interacting with two different adaptive systems adjusting the environmental visual complexity based on EDA. Increased visual complexity led to increased alpha power and alpha-theta ratio, reflecting increased mental fatigue and workload. At the same time, EDA exhibited distinct dynamics with increased tonic and phasic components. Integrating multimodal physiological measures for adaptation evaluation enlarges our understanding of the impact of system adaptation on users' physiology and allows us to account for it and improve adaptive system design and optimization algorithms.