A propos du centre ou de la direction fonctionnelle

The Inria Centre at Rennes University is one of Inria's eight centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.

Contexte et atouts du poste

This internship is not in the context of a funded partnership. However, the intern will part of the SEAMLESS team and co-supervised by three permanent researchers: Léa Pillette, Marc Macé and Anatole Lécuyer as well as a PhD student: Théo Lefeuvre.

The goal of the project is to develop innovative interaction for brain-computer interfaces. Motor imagery-based Brain-Computer Interfaces (MI-BCIs) allow individuals to control digital devices by analyzing brain activity, typically acquired via electroencephalography (EEG). These systems have for instance applications in assistive technologies for people with motor impairments, as well as in gaming. While MI-BCIs show promise, they face challenges in efficiency, with 15-30% of users unable to operate them effectively. Research suggests that improvements in feedback modalities and task instructions could enhance their usability. This internship will explore how the complexity and type of imagined movements affect MI-BCI performance.

No regular travel is foreseen for this position.

Mission confiée

Description of the internship:

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]. For instance, through the use of an MI-BCI a person can control the direction of a wheelchair by imagining right or left-hand movements. MI-BCIs are particularly promising because of their many fields of application. For instance, they have been developed for people that lost all or most of their motor abilities and still have intact mental abilities. They are also used for video-games, virtual reality or smart-home control.

First studies in the field of BCI date back from 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. For instance, 15-30% of users cannot control a sensorimotor imagery based BCI [Lotte et al., 2013]. There are several leads to improve BCI-based technologies. Some focused on the improvement of the feedback that is provided to the person. 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 to improve these technologies is to assess the different imagery tasks that the users are performing. People are currently encouraged to try different sensorimotor imagery tasks (e.g., imagining playing the piano or throwing a ball) [Kober et al., 2013]. Currently, the only recommendation they are provided with is to imagine the sensations associated with the chosen movement [Rimbert et al., 2020]. No information exists on the movements or sensations to be favored, e.g., type, magnitude or duration.

In this context, we propose an internship which aims to investigate the instructions provided to people regarding the movements that they should imagine when training to use MI-BCIs. For instance, the influence of the complexity (number of degrees of liberty or number of articulations involved) of a movement could be assessed by asking participants to imagine movements of different complexity while recording their brain activity using EEG. The open source OpenViBE software will be used to design the MI-BCI. To acquire data regarding the brain activity the student will use electroencephalography, a non-invasive and safe method that acquire electrical residue at the surface of the head.

Main activities:

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 and the neurophysiological basis of movement. Based on this analysis 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 analysis of the results. The final goal is to report all these results in an article written with the rest of the project team.

Step #1

Step #2

Step #3

Step #4

Study of the literature

X

Design of an experimental protocol

X

Implementation of the experimental protocol (using OpenViBE and motion capture)

X

Experiments with healthy participants

X

Statistical and neurophysiological analysis of the results

X

Writing a scientific article

X

References:

Clerc, M., Bougrain, L., & Lotte, F. (Eds.). (2016). BCI 1: Methods and perspectives . John Wiley & Sons.

Kober, S. E., Witte, M., Ninaus, M., Neuper, C., & Wood, G. (2013). Learning to modulate one's own brain activity: the effect of spontaneous mental strategies. Frontiers in human neuroscience , 7 , 695.

Lotte, F., Larrue, F., & Mühl, C. (2013). Flaws in current human training protocols for spontaneous brain-computer interfaces: lessons learned from instructional design. Frontiers in human neuroscience , 7 , 568.

Pillette, L., N'kaoua, B., Sabau, R., Glize, B., & Lotte, F. (2021). Multi-session influence of two modalities of feedback and their order of presentation on MI-BCI user training. MTI , 5 (3), 12.

Rimbert, S., Bougrain, L., & Fleck, S. (2020, October). Learning how to generate kinesthetic motor imagery using a BCI-based learning environment. In 2020 IEEE International Conference SMC (pp. 2483-2498).

Principales activités

Main activities (5 maximum) :

• Literature review
• Experimental design
• Implementation of neurofeedback solutions
• Statistical and neurophysiological analyses
• Write scientific articles

Compétences

Technical skills and level required : Knowledge in programmation

Languages : English

Other valued appreciated : A first experience with Unity, knowledge in cognitive sciences and neurosciences would be a bonus.

Avantages

• • Subsidized meals
  • Social, cultural and sports events and activities