Description du poste

Motor imagery-based Brain-Computer Interfaces (MI-BCIs) introduce promising possibilities for interacting with digital devices only through the analysis of brain activity, often acquired through electroencephalography (EEG) (Clerc et al. 2016). Through the use of an MI-BCI, a person can control the direction of a wheelchair by imagining right or left-hand movements. These interfaces are particularly promising because of their many fields of application. For instance, they have been developed for people who lost all or most of their motor abilities and still have intact mental abilities. Beyond clinical use, MI-BCIs are also used for video-games, virtual reality or smart-home control.

First studies in the field of BCIs date back to the beginning of the century and are thus fairly recent. Their efficiency still has to be improved for the technology to undergo a strong growth outside of research laboratories. Notably, 15-30% of users cannot control a sensorimotor imagery-based BCI (Lotte et al. 2013). There are several leads to improve BCI-based technologies. One key area of focus is optimizing the training protocols users undergo to modulate their brain activity, specifically by improving the feedback provided. In previous research, we have for instance shown that a multimodal feedback composed of vibrotactile and realistic visual stimuli is more efficient than a unimodal one composed of realistic visual stimuli only (Pillette et al. 2021). Another promising approach is the use of enriched feedback provided to the participants with additional information, such as EEG signal stability (Sollfrank et al. 2016) or muscular relaxation state (Schumacher et al. 2015), alongside the system's confidence in the recognized movement. While results regarding performance gains remain mixed, enriched feedback has been shown to enhance user motivation and reduce frustration.

In this context, we propose an internship which aims to investigate innovative feedback provided to people regarding their performance in imagining movements when training to use MI-BCIs. The open source OpenViBE software will be used to design an MI-BCI. To acquire data regarding the brain activity the student will use electroencephalography, a non-invasive and safe method that measures electrical activity at the surface of the head.

Depending on the duration of the internship, the intern will be involved in all or part of the following phases of the project. During a first phase, the student will have to familiarize themself with the literature in BCIs, including MI-BCIs, existing enriched feedback in BCIs, and muscle contamination in the EEG. Based on these analyses of the literature, the student will be involved in the design of an experimental protocol, which they will implement (using OpenViBE and potentially Unity). The student will then pre-test the experimental protocol, perform the experiments and run statistical and neurophysiological analyses of the results. The final goal is to report all these results in an article written with the rest of the project team.

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References:
Clerc, Maureen, Laurent Bougrain, and Fabien Lotte. 2016. Brain–Computer Interfaces 1: Foundations and Methods. Wiley-ISTE. Vol. 1.
Lotte, Fabien, Florian Larrue, and Christian Mühl. 2013. 'Flaws in Current Human Training Protocols for Spontaneous Brain-Computer Interfaces: Lessons Learned from Instructional Design'. Frontiers in Human Neuroscience 7 (September). https://doi.org/10.3389/fnhum.2013.00568.
Pillette, Léa, Bernard N'Kaoua, Romain Sabau, Bertrand Glize, and Fabien Lotte. 2021. 'Multi-Session Influence of Two Modalities of Feedback and Their Order of Presentation on MI-BCI User Training'. Multimodal Technologies and Interaction 5 (3): 12. https://doi.org/10.3390/mti5030012.
Schumacher, Julia, Camille Jeunet, and Fabien Lotte. 2015. 'Towards Explanatory Feedback for User Training in Brain-Computer Interfaces'. 2015 IEEE International Conference on Systems, Man, and Cybernetics, October, 3169–74. https://doi.org/10.1109/SMC.2015.550.
Sollfrank, T., A. Ramsay, S. Perdikis, et al. 2016. 'The Effect of Multimodal and Enriched Feedback on SMR-BCI Performance'. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology 127 (1): 490–98. https://doi.org/10.1016/j.clinph.2015.06.004.

Description de l'employeur

Today, IRISA is one of the largest French research laboratories (with over 850 staff members) in the field of computer science and information technology. Structured into seven scientific departments, IRISA is a center of excellence whose scientific priorities include bioinformatics, system security, new software architectures, virtual reality, big data analysis, and artificial intelligence. Focused on the future of computer science and necessarily internationally oriented, IRISA is at the very heart of society's digital transition and innovation in the service of cybersecurity, health, the environment and ecology, transport, robotics, energy, culture, and artificial intelligence.

Presentation of the CNRS as an employer: https://www.cnrs.fr/fr/le-cnrs
Presentation of IRISA as the host laboratory: https://www.irisa.fr/umr-6074

This position is located in a sector subject to the Protection of Scientific and Technical Potential (PPST) and therefore requires, in accordance with regulations, that your arrival be authorized by the competent authority of the Ministry of Higher Education and Research.

Descriptif du profil recherché

We are looking for a motivated candidate with a good level of English and one of the following profiles:
• Profile 1: Strong background in cognitive science, experimental sciences, and neurophysiology, ideally with knowledge in computer science and signal processing.
• Profile 2: Strong background in computer science and signal processing, ideally with knowledge in cognitive science, experimental sciences, and neurophysiology.

Conditions particulières d'exercice

NA

Langues

Good level of English required.