BrainConquest: Boosting Brain-Computer Communication with High Quality User Training

ERC Starting Grant, 2017-2022

PI: Fabien Lotte


  • ERC Starting Grant (PI: Fabien Lotte)
  • Date: 2017-2022
  • Abstract:

Brain-Computer Interfaces (BCIs) are communication systems that enable users to send commands to computers through brain signals only, by measuring and processing these signals. Making computer control possible without any physical activity, BCIs have promised to revolutionize many application areas, notably assistive technologies, e.g., for wheelchair control, and man-machine interaction. Despite this promising potential, BCIs are still barely used outside laboratories, due to their current poor reliability. For instance, BCIs only using two imagined hand movements as mental commands decode, on average, less than 80% of these commands correctly, while 10 to 30% of users cannot control a BCI at all.

A BCI should be considered a co-adaptive communication system: its users learn to encode commands in their brain signals (with mental imagery) that the machine learns to decode using signal processing. Most research efforts so far have been dedicated to decoding the commands. However, BCI control is a skill that users have to learn too. Unfortunately how BCI users learn to encode the commands is essential but is barely studied, i.e., fundamental knowledge about how users learn BCI control is lacking. Moreover standard training approaches are only based on heuristics, without satisfying human learning principles. Thus, poor BCI reliability is probably largely due to highly suboptimal user training.

In order to obtain a truly reliable BCI we need to completely redefine user training approaches. To do so, I propose to study and statistically model how users learn to encode BCI commands. Then, based on human learning principles and this model, I propose to create a new generation of BCIs which ensure that users learn how to successfully encode commands with high signal-to-noise ratio in their brain signals, hence making BCIs dramatically more reliable. Such a reliable BCI could positively change man-machine interaction as BCIs have promised but failed to do so far.

Project members:
PhD students:

  • Léa Pillette 2017-2019  (partly funded by the ERC)
  • Jelena Mladenovic 2017-2019 (party funded by the ERC)
  • Aurélien Appriou 2017-2020
  • Camille Benaroch 2018-2021
  • Aline Roc 2019-2022


  • Khadijeh Sadatnejad 2019-2021
  • Sébastien Rimbert 2021-2022
  • Cécilia Ostertag 2022


  • Thibaut Monseigne 2019-2022
  • Pauline Dreyer 2021-2022

Visiting students:

  • Mehdi Bugallo 2018
  • Satyam Kumar 2018
  • Zachary Traylor 2022-2023

Master student interns:

  • Aline Roc 2018
  • Camille Benaroch 2018
  • Romain Sabau 2019
  • Alina Lushnikova 2020
  • David Trocellier 2020
  • Eidan Tzdaka 2020
  • Lena Kolodzienski 2020
  • Nibras Abo Alzahab 2021
  • Alper Er 2021
  • Smeety Pramij 2019 & 2021
  • Jordan Azzouguen 2022

Selected publications:



  • S. Rimbert, F. Lotte, “How ERD modulations during motor imageries relate to users’ traits and BCI performances“, IEEE EMBS Engineering in Medecine and Biology Conference (EMBC’22), 2022 – pdf
  • L. Bougrain, S. Rimbert, P.L.C. Rodrigues, G. Canron, F. Lotte, “Guidelines to use Transfer Learning for Motor Imagery Detection: an experimental study“, 10th International IEEE/EMBS Conference on Neural Engineering (NER), 2021 – pdf
  • M.S. Yamamoto, K. Sadatnejad, R. M. Islam, F. Lotte, T. Tanaka. “Reliable outlier detection by spectral clustering on Riemannian manifold of EEG covariance matrix”, 8th International  BCI meeting, 2021 – best student poster award pdf
  • A. Appriou, J. Ceha, S. Pramij, D. Dutartre, E. Law, P.-Y. Oudeyer, F. Lotte, “Towards measuring states of epistemic curiosity through electroencephalographic signals”, IEEE Systems, Man & Cybernetics (IEEE SMC’20) conference, 2020 – pdf
  • C. Benaroch, C. Jeunet, F. Lotte, “Are users’ traits informative enough to predict/explain their mental-imagery based BCI performances?”,  8th International Graz Brain-Computer Interface Conference, 2019 – pdf

Open-Source Software:

Open access data:

  • Dreyer Pauline, Roc Aline, Rimbert Sébastien, Pillette Léa, & Lotte Fabien. (2023). A large EEG database with users’ profile information for motor imagery Brain-Computer Interface research (To use (2)) [Data set]. Zenodo.

Comments are closed.