Design of a Virtual Reality Adaptive System based on Electrodermal Activity phasic components

Description

Electrodermal activity (EDA) denotes the measurement of continuous changes in the electrical conductance properties of the skin in response to sweat secretion by the sweat glands. EDA is autonomously modulated by sympathetic nervous system (SNS) activity, a component of the autonomic nervous system (ANS), which is involved in the control of involuntary bodily functions as well as cognitive and emotional states. Specifically, phasic EDA activity correlated with stress, cognitive load, and attention orienting. Therefore, measuring phasic EDA responses can give us information about the user's state. In this thesis project, we want to develop an adaptive system that modifies the visual complexity of the VR environment based on changes in phasic EDA. Specifically, we want to use new signal processing methodologies termed adaptive thresholding and gaussian filtering. The research consists of three main stages: (1) validation of the psychophysiological inference underpinning the adaptive system (2) implementation of a working VR prototype, and (3) an evaluation of the adaptive environment.

You will

• Perform a literature review
• Modify an existing VR environment
• Implement an preprocessing pipeline for phasic EDA detection
• Collect and analyze electroencephalographic (EEG), electrodermal activity (EDA) and electrocardiography (ECG) data
• Summarize your findings in a thesis and present them to an audience
• (Optional) co-writing a research paper

You need

• Strong communication skills in English
• Good knowledge of Unity
• Good knowledge of Python libraries for scientific computing (e.g. Scipy, MNE).

References

• Fairclough, S. H. (2009). Fundamentals of physiological computing. Interacting with computers, 21(1-2), 133-145.
• Chiossi, F., Welsch, R., Villa, S., Chuang, L., & Mayer, S. (2022). Virtual Reality Adaptation Using Electrodermal Activity to Support the User Experience. Big Data and Cognitive Computing, 6(2), 55.
• Babaei, E., Tag, B., Dingler, T., & Velloso, E. (2021, May). A critique of electrodermal activity practices at chi. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-14).
• Kleckner, I., Wormwood, J. B., Jones, R. M., Siegel, E., Culakova, E., Heathers, J., ... & Goodwin, M. (2021). Adaptive thresholding increases ability to detect changes in rate of skin conductance responses to psychologically arousing stimuli.