Evaluation of an Adaptive VR environment that Uses EEG Measures as Inputs to a Biocybernetic Loop

Description

Biocybernetic adaptation is a form of physiological computing where real-time physiological data from the brain and the body can be used as an input to adapt the user interface. In this way, from the physiological data, we can infer the user’s state and design implicit interactions in VR to change the scene to support certain goals. This thesis aims the develop and evaluate an adaptive VR environment designed to maximize users' performance by exploiting changes in real-time electroencephalography (EEG) to adjust the level of visual complexity. The research consists of three main stages: (1) validation of the input EEG measures underpinning the loop; (2) implementation of a working VR prototype; and (3) an evaluation of the adaptive environment. Specifically, we aim to demonstrate the sensitivity of EEG power in the (frontal) theta and (parietal) alpha bands to adapt levels of visual complexity.

You will

• Perform a literature review
• Modify an existing VR environment
• Implement an online biocybernetic loop using EEG
• Collect and analyze EEG, EDA, and 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 and/or C#
• Good knowledge of Python libraries for scientific computing (e.g. Scipy, MNE).

References

• Biocybernetics Loops and Physiological Computing
• Development of an Adaptive Game using EEG frequencies