@inproceedings{chiossi2023exploring,
	title = {Exploring Physiological Correlates of Visual Complexity Adaptation: Insights from EDA, ECG, and EEG Data for Adaptation Evaluation in VR Adaptive Systems},
	author = {Francesco Chiossi and Changkun Ou and Sven Mayer},
	year = {2023},
	booktitle = {Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	series = {CHI EA '23},
	doi = {10.1145/3544549.3585624},
	url = {https://sven-mayer.com/wp-content/uploads/2023/03/chiossi2023exploring.pdf},
	date = {2023-04-23},
	abstract = {Physiologically-adaptive Virtual Reality can drive interactions and adjust virtual content to better fit users' needs and support specific goals. However, the complexity of psychophysiological inference hinders efficient adaptation as the relationship between cognitive and physiological features rarely show one-to-one correspondence. Therefore, it is necessary to employ multimodal approaches to evaluate the effect of adaptations. In this work, we analyzed a multimodal dataset (EEG, ECG, and EDA) acquired during interaction with a VR-adaptive system that employed EDA as input for adaptation of secondary task difficulty. We evaluated the effect of dynamic adjustments on different physiological features and their correlation. Our results show that when the adaptive system increased the secondary task difficulty, theta, beta, and phasic EDA features increased. Moreover, we found a high correlation between theta, alpha, and beta oscillations during difficulty adjustments. Our results show how specific EEG and EDA features can be employed for evaluating VR adaptive systems.},
	keywords = {electrodermal activity, electromyography, physiological computing, physiological sensing, virtual reality}
}