Inria
Innovative Feedback for Motor Imagery-based Brain-Computer Interfaces
Inria, Mission, Kansas, United States
Innovative Feedback for Motor Imagery-based Brain-Computer Interfaces
The 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. 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 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. 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). 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. 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, 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 activities (5 maximum) : Literature review Implementation of neurofeedback solutions Statistical and neurophysiological analyses Write scientific articles 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. Avantages
Social, cultural and sports events and activities Please submit online : your resume, cover letter and letters of recommendation eventually We are looking for a motivated candidate with knowledge in computer science, neurosciences and/or human-machine interfaces and a good level of English.
#J-18808-Ljbffr
The 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. 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 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. 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). 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. 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, 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 activities (5 maximum) : Literature review Implementation of neurofeedback solutions Statistical and neurophysiological analyses Write scientific articles 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. Avantages
Social, cultural and sports events and activities Please submit online : your resume, cover letter and letters of recommendation eventually We are looking for a motivated candidate with knowledge in computer science, neurosciences and/or human-machine interfaces and a good level of English.
#J-18808-Ljbffr