Project in EEG, Brain–Computer Interfaces, and Embodiment in Immersive Virtual Reality
This doctoral project aims to develop and study an immersive approach combining virtual reality, embodied avatars, EEG, and brain–computer interfaces in order to create an interactive loop between the user’s brain activity, motor intentions, and the feedback received in a virtual environment.
EEG-based brain–computer interfaces have significant potential for rehabilitation, motor training, and the study of bodily embodiment mechanisms. However, several challenges remain: EEG signals are noisy, vary substantially across individuals, and are often difficult to exploit in real time. In addition, the feedback provided to users is sometimes not very engaging or insufficiently connected to a realistic bodily experience. Immersive virtual reality offers a way to address these limitations by integrating brain signals into richer, more embodied, and interactive environments.
The project will explore how a VR system based on an embodied avatar can be coupled with EEG measures and classification or adaptation algorithms to provide meaningful sensorimotor feedback. The general objective is to design, implement, and evaluate a closed-loop system in which the user’s state or intention influences the virtual environment, and in which immersive feedback may in turn support engagement, motor learning, or rehabilitation.
In collaboration with the Institut de réadaptation Gingras-Lindsay-de-Montréal (IRGLM), the project will seek to develop immersive and neurotechnological approaches with potential applications in motor rehabilitation, while maintaining a strong methodological and experimental engineering component. The precise experimental protocol will be defined with the recruited student and the project partners.
The PhD will typically include:
- Technological development in immersive virtual reality;
- EEG acquisition and signal processing;
- Machine learning / signal classification;
- Real-time integration;
- Experimentations with human participants;
- Quantitative analysis involving behavioural, neurophysiological, and subjective data.
The project is part of the laboratory’s broader research on avatar embodiment, movement modulation in virtual reality, sensorimotor feedback, and neurotechnologies applied to health.
Required knowledge
The candidate should be seeking admission to a PhD program. A master’s application may be considered only in the case of an exceptionally strong profile that is directly relevant to the project.
The ideal candidate will combine some of the following skills and experience:
- background in engineering, computer science, biomedical engineering, computational neuroscience, health technologies, or a related field;
- experience in signal processing, ideally EEG, EMG, or other physiological signals;
- knowledge of machine learning, classification, deep learning, or time-series analysis;
- strong interest in brain–computer interfaces, virtual reality, and real-time interactive systems;
- programming skills, for example in Python, MATLAB, C#, C++, or equivalent;
- experience with Unity, Unreal, or VR development: an important asset;
- experience with human participants, experimental protocols, questionnaires, or statistical analysis: an asset;
- autonomy, scientific rigour, and strong reading and writing skills in English;
- interest in an interdisciplinary project combining technology, cognition, human–computer interaction, and health.
Prior direct experience in EEG/BCI is strongly preferred, but candidates with an excellent background in signal processing, machine learning, or biomedical engineering will also be considered.