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BrainConquest SIGNED

Boosting Brain-Computer Communication with high Quality User Training

Total Cost €


EC-Contrib. €






Project "BrainConquest" data sheet

The following table provides information about the project.


Organization address
postcode: 78153

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country France [FR]
 Total cost 1˙498˙751 €
 EC max contribution 1˙498˙751 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2016-STG
 Funding Scheme ERC-STG
 Starting year 2017
 Duration (year-month-day) from 2017-07-01   to  2022-06-30


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

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 human-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 human-machine interaction as BCIs have promised but failed to do so far.


List of deliverables.
Data Management Plan Open Research Data Pilot 2020-03-11 14:35:07

Take a look to the deliverables list in detail:  detailed list of BrainConquest deliverables.


year authors and title journal last update
List of publications.
2020 Aurélien Appriou, Andrzej Cichocki, Fabien Lotte
Modern machine learning algorithms to classify cognitive and affective states from electroencephalography signals
published pages: , ISSN: 2333-942X, DOI:
IEEE systems, man, and cybernetics magazine 2020-02-28
2020 Léa Pillette, Camille Jeunet, Boris Mansencal, Roger N’Kambou, Bernard N’Kaoua, Fabien Lotte
A physical learning companion for Mental-Imagery BCI User Training
published pages: 102380, ISSN: 1071-5819, DOI: 10.1016/j.ijhcs.2019.102380
International Journal of Human-Computer Studies 136 2020-02-28
2019 Roc, Aline; Pillette, Léa; N\'Kaoua, B.; Lotte, Fabien
Would Motor-Imagery based BCI user training benefit from more women experimenters?
published pages: , ISSN: , DOI:
8th Graz Brain-Computer Interface Conference 2019 2020-02-28
2020 Khadijeh Sadatnejad, Aline Roc, Léa Pillette, Aurélien Appriou, Thibaut Monseigne, Fabien Lotte
Channel selection over riemannian manifold with non-stationarity consideration for brain-computer interface applications
published pages: , ISSN: , DOI:
Proceedings of ICASSP 2020 2020-02-28
2019 J.-M. Batail, S. Bioulac, F. Cabestaing, C. Daudet, D. Drapier, M. Fouillen, T. Fovet, A. Hakoun, R. Jardri, C. Jeunet, F. Lotte, E. Maby, J. Mattout, T. Medani, J.-A. Micoulaud-Franchi, J. Mladenovic, L. Perronet, L. Pillette, T. Ros, F. Vialatte
EEG neurofeedback research: A fertile ground for psychiatry?
published pages: 245-255, ISSN: 0013-7006, DOI: 10.1016/j.encep.2019.02.001
L\'Encéphale 45/3 2020-02-28
2019 Benaroch, Camille; Jeunet, Camille; Lotte, Fabien
Are users\' traits informative enough to predict/explain their mental-imagery based BCI performances ?
published pages: , ISSN: , DOI:
8th Graz BCI Conference 2019, Sep 2019, Graz, Austria 2020-02-28
2020 Jelena Mladenovic, Jeremy Frey, Mateus Joffily, Emmanuel Maby, Fabien Lotte, Jeremie Mattout
Active inference as a unifying, generic and adaptive framework for a P300-based BCI
published pages: 16054, ISSN: 1741-2552, DOI: 10.1088/1741-2552/ab5d5c
Journal of Neural Engineering 17/1 2020-02-28
2020 Fabien Lotte, Camille Jeunet, Ricardo Chavarriaga, Laurent Bougrain, Dave E. Thompson, Reinhold Scherer, Md Rakibul Mowla, Andrea Kübler, Moritz Grosse-Wentrup, Karen Dijkstra, Natalie Dayan
Turning negative into positives! Exploiting ‘negative’ results in Brain–Machine Interface (BMI) research
published pages: 1-12, ISSN: 2326-263X, DOI: 10.1080/2326263x.2019.1697143
Brain-Computer Interfaces 2020-02-28
2018 Fabien Lotte, Camille Jeunet
Defining and quantifying users’ mental imagery-based BCI skills: a first step
published pages: 46030, ISSN: 1741-2560, DOI: 10.1088/1741-2552/aac577
Journal of Neural Engineering 15/4 2019-09-02
2018 Andreas Meinel, Sebastián Castaño-Candamil, Benjamin Blankertz, Fabien Lotte, Michael Tangermann
Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems
published pages: , ISSN: 1539-2791, DOI: 10.1007/s12021-018-9396-7
Neuroinformatics 2019-09-02
2018 Léa Pillette, Aurélien Appriou, Andrzej Cichocki, Bernard N\'Kaoua, Fabien Lotte
Classification of attention types in EEG signals
published pages: , ISSN: , DOI:
International BCI Meeting 2019-09-02
2018 Appriou , Aurélien; Pillette , Léa; Cichocki , Andrzej; Lotte , Fabien
BCPy, an open-source python platform for offline EEG signals decoding and analysis
published pages: , ISSN: , DOI:
International BCI Meeting, May 2018, Pacific Grove, United States 1 2019-09-02
2018 Camille Jeunet, Fabien Lotte, Jean-Marie Batail, Pierre Philip, Jean-Arthur Micoulaud Franchi
Using Recent BCI Literature to Deepen our Understanding of Clinical Neurofeedback: A Short Review
published pages: 225-233, ISSN: 0306-4522, DOI: 10.1016/j.neuroscience.2018.03.013
Neuroscience 378 2019-09-02
2018 Laurens R. Krol, Juliane Pawlitzki, Fabien Lotte, Klaus Gramann, Thorsten O. Zander
SEREEGA: Simulating event-related EEG activity
published pages: 13-24, ISSN: 0165-0270, DOI: 10.1016/j.jneumeth.2018.08.001
Journal of Neuroscience Methods 309 2019-09-02
2018 Felix Putze, Christian Mühl, Fabien Lotte, Stephen Fairclough, Christian Herff
Editorial: Detection and Estimation of Working Memory States and Cognitive Functions Based on Neurophysiological Measures
published pages: , ISSN: 1662-5161, DOI: 10.3389/fnhum.2018.00440
Frontiers in Human Neuroscience 12 2019-09-02
2018 Lotte , Fabien; Jeunet , Camille; Mladenovic , Jelena; N \'kaoua , Bernard; Pillette , Léa
A BCI challenge for the signal processing community: considering the user in the loop
published pages: , ISSN: , DOI: 1 2019-09-02
2018 F Lotte, L Bougrain, A Cichocki, M Clerc, M Congedo, A Rakotomamonjy, F Yger
A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update
published pages: 31005, ISSN: 1741-2560, DOI: 10.1088/1741-2552/aab2f2
Journal of Neural Engineering 15/3 2019-09-02
2018 Pillette , Léa; Jeunet , Camille; N \'kambou , R; N\'Kaoua , Bernard; Lotte , Fabien
Towards Artificial Learning Companions for Mental Imagery-based Brain-Computer Interfaces
published pages: , ISSN: , DOI:
WACAI 2018 2019-09-02
2017 Lotte , Fabien; Cichocki , Andrzej
What are the best motor tasks to use and calibrate SensoriMotor Rhythm Neurofeedback and Brain-Computer Interfaces? A preliminary case study
published pages: , ISSN: , DOI:
rtFIN 2017 - Real-time functional Imaging and Neurofeedback conference 1 2019-09-02
2018 Lotte , Fabien; Cichocki , Andrzej
Can transfer learning across motor tasks improve motor imagery BCI?
published pages: , ISSN: , DOI:
International BCI Meeting 2018 1 2019-09-02
2019 Satyam Kumar, Florian Yger, Fabien Lotte
Towards Adaptive Classification using Riemannian Geometry approaches in Brain-Computer Interfaces
published pages: , ISSN: , DOI:
IEEE International Winter Conference on Brain-Computer Interfaces 2019-09-02

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