Context and team background

Although the concept of Brain-Computer Interface (BCI) had been introduced by VIDAL in 1973, it really emerged as a new field of research in the early nineties when systems allowing real-time processing of brain signals became available. Since then, BCI research has seen an impressive growth in the domains of neuroscience, computer science, and clinical research. However, there are still a significant number of theoretical and technological issues that have to be dealt with, probably with a maximum efficiency if following an interdisciplinary approach.

Brain-computer interfacing has been listed by the ISTAG of EU as one of the most promising emerging technologies of the Neuro-ICT program for 2020 and beyond. In France, the Alliance nationale pour les sciences de la santé et de la vie and the Agence de la biomédecine also plan to foster the emergence of a French network of research teams working on BCI. The same goal has been displayed for many years by the GDR STIC-Santé research group.

In this context, a BCI is considered as an effective tool for rehabilitation and/or assistance of severely impaired patients. Developing such BCIs is the aim of our team, in which every member has a specific background on a theoretical field that is an asset for success: spatio-temporal signal processing, evidential reasoning and sensor fusion, data classification and clustering, constraint-based programming and human-computer interaction through multimodal interfaces.

Objective #1: development of hybrid BCIs

To improve the speed and robustness of communication, Leeb et al. have introduced recently the so-called “hybrid BCI”, in which brain activity and one or more other signals are analyzed jointly. Up to our knowledge, no team is working specifically on assistance-oriented hydrid BCIs in France. The Hybrid joint-research team at INRIA Rennes develops hybrid BCIs, but mainly for interaction in 3D virtual worlds.

We investigate the field of hybrid BCIs toward two main directions. Firstly, we consider that all the channels of a hybrid BCI carry the same information drowned out by different types of noise, and apply sensor fusion techniques to improve the signal to noise ratio and therefore the robustness of the interface. Secondly, we consider that every channel carries a piece of the global information, and analyze the multimodal interaction paradigm using CARE properties (Complementarity, Assignment, Redundancy, and Equivalence).

Objective #2: assistive technology for people suffering from muscular dystrophy

We devote a great deal of energy to transferring to clinical experiments the hybrid BCIs that we develop in the team. Our first target population will be people suffering from Duchenne Muscular Dystrophy (DMD). In the early stages of this progressive disease, the patient is still able to use his muscles. Thus, he can be trained to use a hybrid BCI with channels measuring his motion, his muscle activities through electromyography, as well as the activity of his motor and pre-motor cortical areas. Later, with the disease evolution, motion-related channels will become less informative and the challenge will be to allow the control channels related to brain activity to take over.

The foundations of this project, unique according to our knowledge, have been laid with colleagues from two services of the CHRU in Lille. Clinicians of the physical medicine and rehabilitation service will define user needs, recruit and manage DMD patients for long term experiments. Colleagues of the clinical neurophysiology service help us define the most appropriate markers of cortical activity for each interaction task.