Innovative Feedback for Motor Imagery-based Brain-Computer Interfaces

Innovative Feedback for Motor Imagery-based Brain-Computer InterfacesLevel of qualifications required : Master\'s or equivalentFonction : Internship ResearchAbout the research centre or Inria departmentThe Inria center at the University of Rennes is one of eight Inria centers and has more than thirty research teams. The Inria center is a major and recognized player in the field of digital sciences. It is at the heart of a rich ecosystem of R&D and innovation, including highly innovative SMEs, large industrial groups, competitiveness clusters, research and higher education institutions, centers of excellence, and technological research institutes.ContextThis 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 post-doctorant:JimmyPetit.The goal of the project is to develop innovative feedback 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 could enhance their usability. This internship will explore the use of enriched feedback provided to the participants with additional information, such as EEG signal stability or muscular relaxation state, alongside the system’s confidence in the recognized movement.No regular travel is foreseen for this position.AssignmentDescription 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). 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.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, 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.Study of the literatureDesign of an experimental protocolImplementation of the experimental protocol (using OpenViBE and motion capture)Experiments with healthy participantsStatistical and neurophysiological analysis of the resultsWriting a scientific articleReferences: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.Main activitiesMain activities (5 maximum) :Literature reviewImplementation of neurofeedback solutionsStatistical and neurophysiological analysesWrite scientific articlesSkillsWe 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.Benefits packageSocial, cultural and sports events and activitiesTheme/Domain :Interaction and visualizationInformation system (BAP E)Warning : you must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed.Instruction to applyPlease submit online: your resume, cover letter and letters of recommendation eventuallyDefence Security : This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST). Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.Recruitment Policy : As part of its diversity policy, all Inria positions are accessible to people with disabilities.We are looking for a motivated candidate with knowledge in computer science, neurosciences and/or human-machine interfaces and a good level of English.About InriaInria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact. #J-18808-Ljbffr