REBEL: REdefining Brain-Computer Interfaces to Enable their users to achieve controL mastery


ANR Jeune Chercheur Jeune Chercheuse Project

Period : 2016-2019

Partners: Inria Bordeaux Sud-Ouest, team Potioc, Laboratoire Handicap, Cognition, Santé, Université de Bordeaux

Funding: ANR

Abstract: Brain-Computer Interfaces (BCI) are communication systems that enable their users to send commands to computers through brain activity only. While BCI are very promising for assistive technologies or human-computer interaction (HCI), they are barely used outside laboratories, due to a poor reliability. Designing a BCI requires 1) its user to learn to produce distinct brain activity patterns and 2) the machine to recognize these patterns using signal processing. Most research efforts focused on signal processing. However, BCI user training is as essential but is only scarcely studied and based on heuristics that do not satisfy human learning principles. Thus, currently poor BCI reliability is probably due to suboptimal user training. Thus, we propose to create a new generation of BCI that apply human learning principles in their design to ensure the users can learn high quality control skills, hence making BCI reliable. This could change HCI as BCI have promised but failed to do so far.

Members:

  • Fabien Lotte, Inria, Project Leader
  • Bernard N’Kaoua, University of Bordeaux
  • Camille Jeunet, Inria / EPFL (former PhD student in the project – current external collaborator)
  • Léa Pillette, Inria, PhD student
  • Thibaut Monseigne, Engineer
  • Aline Roc, Inria (former Master student in the project – February-July 2018 – not fundeed by the project)
  • Suzy Teillet, Inria (former Master student in the project – February-June 2016)

Publications

Journal articles:

  1. Batail, J.-M., Bioulac, S., Cabestaing, F., Daudet, C., Drapier, D., Fouillien, M., Fovet, T., Hakoun, A., Jardri, R., Jeunet, C., Lotte, F., Maby, E., Mattout, J., Medani, T., Micoulaud-Franchi, J.A., Mladenovic, J., Perronet, L., Pillette, L., Ros, T., Vialatte, F. (authors in alphabetical orders), « Neurofeedback research: a fertile ground for psychiatry? », L’Encéphale, vol. 843, no. 1, pp. 1-68, 2019 – link
  2. A. Meinel, S. Castaño-Candamil, B. Blankertz, F. Lotte, M. Tangermann, “Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems“, Neuroinformatics, 2018 – accepted – draft pdf – final version – code
  3. F. Lotte, C. Jeunet, “Defining and Quantifying Users’ Mental Imagery-based BCI skills: a first step“, Journal of Neural Engineering, vol. 15, no. 4, 2018 – link – pdf – code
  4. C. Jeunet, F. Lotte, J.-M. Batail, P. Philip, J.-A. Micoulaud-Franchi, “Using recent BCI literature to deepen our understanding of clinical neurofeedback: a short review”, Neuroscience, vol. 378, pp. 225-23, 2018 – pdf
  5. F Lotte, L Bougrain, A Cichocki, M Clerc, M Congedo, A Rakotomamonjy, and F Yger, “A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update“, Journal of Neural Engineering, vol. 15, no. 3, 2018 – link – pdf
  6. T. Fovet, J.-A. Micoulaud-Franchi, F.-B. Vialatte, F. Lotte, C. Daudet, J.-M. Batail, J. Mattout, G. Wood, R. Jardri, S. Enriquez-Geppert, T. Ros, “On assessing neurofeedback effects: should double-blind replace neurophysiological mechanisms?”, Letter to the Editor, Brain, 2017 – pdf
  7. F. Yger, M. Bérar, F. Lotte, “Riemannian approaches in Brain-Computer Interfaces: a review”, IEEE Transactions on Neural System and Rehabilitation Engineering, 2017 – pdf
  8. M. Arns, J.-M. Batail, S. Bioulac, M. Congedo, C. Daudet, D. Drapier, T. Fovet, R. Jardri, M. Le Van Quyen, F. Lotte, D. Mehler, J.-A. Micoulaud-Franchi, D. Purper-Ouakil, F. Vialatte, The Next Group, “Neurofeedback: one of today’s techniques in psychiatry?”, L’Encéphale, 2017 – pdf
  9. Chavarriaga, M. Fried-Oken, S. Kleih, F. Lotte, R. Scherer, “Heading for new shores! Overcoming pitfalls in BCI design”, Brain-Computer Interfaces, pp. 1-14, 2016 – pdf

Conference articles:

  1. A. Roc, L. Pillette, B. N’Kaoua, F. Lotte, “Would Motor-Imagery based BCI user training benefit from more women experimenters?”, 8th Graz Brain-Computer Interface Conference, 2019 – pdf
  2. S. Kumar, F. Yger, F. Lotte, “Towards Adaptive Classification using Riemannian Geometry approaches in Brain-Computer Interfaces“, IEEE International Winter Conference on Brain-Computer Interfaces, 2019 – pdf – code
  3. L. Pillette, C. Jeunet, R. N’Kambou, B. N’Kaoua & F. Lotte, “Towards artificial learning companions for mental imragery-based brain-computer interfaces”, Workshop on “Affects, Artificial Companions and Interaction” (WACAI 2018), 2018 – pdf
  4. C. Jeunet, B. N’Kaoua & F. Lotte, “Towards a cognitive model of MI-BCI user training”, 7th international BCI conference, 2017 – pdf
  5. F. Lotte, C. Jeunet, “Online classification accuracy is a poor metric to study mental-imagery based BCI user learning: An experimental demonstration and new metrics”, 7th international BCI conference, 2017 – pdf
  6. L. Pillette, C. Jeunet, B. Mansencal, R. N’Kambou, B. N’Kaoua, F. Lotte, “PEANUT : Personalised Emotional Agent for Neurotechnology User-Training”, 7th international BCI conference, 2017 – pdf
  7. J. Mladenovic, J. Frey, M. Bonnet-Save, J. Mattout, F. Lotte, “The Impact of Flow in an EEG-based Brain Computer Interface”, 7th international BCI conference, 2017
  8. S. Teillet, F. Lotte, B. N’Kaoua, C. Jeunet, “Towards a Spatial Ability Training to Improve Motor Imagery based Brain-Computer Interfaces (MI-BCIs) Performance: a Pilot Study“, IEEE International Conference on Systems Man and Cybernetics (IEEE SMC), 2016 – pdf

Book chapters:

  1. F. Lotte, C. Jeunet, J. Mladenovic, B. N’Kaoua, L. Pillette, « A BCI challenge for the signal processing community: considering the human in the loop », IET Book ‘Signal Processing and Machine Learning for Brain-Machine Interfaces’, Eds Tanaka & Arvaneh, 2018 – pdf
  2. Lotte, CS Nam, A Nijholt, “Evolution of Brain-Computer Interfaces”, BCI Handbook, Taylor & Francis, 2018
  3. J Mladenović, J Mattout, and F Lotte, “A Generic Framework for Adaptive EEG-Based BCI Training and Operation”, BCI Handbook, Taylor & Francis, 2018
  4. C Jeunet, S Debener, F Lotte, J Mattout, R Scherer, and C Zich, “Mind the Traps: Design Guidelines for Rigorous BCI Experiments”, BCI Handbook, Taylor & Francis, 2018

Abstract and short papers:

  1. A Roc, L Pillette, B N’Kaoua, F Lotte, “Do experimenters have an influence on MI-BCI user training?”, CORTICO days, 2019
  2. L Pillette, B Glize, B N’Kaoua, PA Joseph, C Jeunet, F Lotte, “Impact of MI-BCI feedback for post-stroke and neurotypical people”, CORTICO days, 2019
  3. L. Pillette, A. Appriou, A. Cichocki, B. N’Kaoua, F. Lotte, “Classification of attention types in EEG signals”, International BCI Meeting 2018 – pdf
  4. F. Lotte, A. Cichocki, “Can transfer learning across motor tasks improve motor imagery BCI?”, International BCI Meeting 2018 – pdf
  5. F. Lotte, A. Cichocki, “What are the best motor tasks to use and calibrate SensoriMotor Rhythm Neurofeedback and Brain-Computer Interfaces? A preliminary case study“, Real-time functional Imaging and Neurofeedback conference (RTFIN’2017), 2017 – pdf
  6. F. Lotte, A. Cichocki, “Improving EEG Neurofeedback with Advanced Machine Learning and Signal Processing tools from Brain-Computer Interfaces Research“, Workshop, Real-time functional Imaging and Neurofeedback conference (RTFIN’2017), 2017 – pdf
  7. J. Frey, C. Jeunet, J. Mladenovic, L. Pillette, F. Lotte, “When HCI Meets Neurotechnologies: What You Should Know about Brain-Computer Interfaces”, Course at ACM CHI 2017, 2017 – pdf
  8. C. Jeunet, F. Lotte, M. Hachet, S. Subramanian, B. N’Kaoua, “Spatial Abilities Play a Major Role in BCI Performance“, International BCI meeting, 2016 – pdf
  9. C. Jeunet, B. N’Kaoua, R. N’Kambou, F. Lotte, “Why and How to Use Intelligent Tutoring Systems to Adapt MI-BCI Training to Each User?”, International BCI meeting, 2016 – pdf

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