Bilateral Contracts and Grants with Industry
Bilateral Contracts and Grants with Industry

Section: Partnerships and Cooperations

European Initiatives

FP7 & H2020 Projects

  • Program: ERC Starting Grant

  • Project acronym: BrainConquest

  • Project title: Boosting Brain-Computer Communication with High Quality User Training

  • Duration: 07/2017-06/2022

  • Coordinator: Fabien Lotte

  • 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.

Collaborations in European Programs, Except FP7 & H2020

  • Program: DGA-DSTL Project

    • Project title: Assessing and Optimising Human-Machine Symbiosis through Neural signals for Big Data Analytics

    • Duration: 2014-2018

    • Coordinator: Damien Coyle and Fabien Lotte

    • Partners: Ulster University, UK, Potioc, France

    • Abstract: This project objective is to design new tools for Big Data analysis, and in particular visual analytics tools that tap onto human cognitive skills as well as on Brain-Computer Interfaces. The goal is to enable the user to identify and select relevant information much faster than what can be achieved by using automatic tools or traditional human-computer interfaces. More specifically, this project will aim at identifying in a passive way various mental states (e.g., different kinds of attention, mental workload, relevant stimulus perception, etc.) in order to optimize the display, the arrangement of the selection of relevant information.

  • Program: ERASMUS+

    • Project acronym: VISTE

    • Project title: Empowering spatial thinking of students with visual impairment

    • Duration: 2016-2019

    • Coordinator: National Technical University of Athens (Greece)

    • Local coordinator: Anke Brock

    • Other partners: Intrasoft International SA (Greece), Casa Corpolui Didatic Cluj (Romania), Liceul Special pentru Deficienti de Vedere Cluj-Napoca (Romania), Eidiko Dimotiko Sxolio Tiflon Kallitheas (Greece)

    • Abstract: VISTE addresses inclusion and diversity through an innovative, integrated approach for enhancing spatial thinking focusing on the unique needs of students with blindness or visual impairment. However, since spatial thinking is a critical competence for all students, the VISTE framework and associated resources and tools will focus on cultivating this competence through collaborative learning of spatial concepts and skills both for sighted and visually impaired students to foster inclusion within mainstream education. The VISTE project will introduce innovative educational practices for empowering students with blindness or visual impairment with spatial skills through specially designed educational scenarios and learning activities as well as through a spatial augmented reality prototype to support collaborative learning of spatial skills both for sighted and visually impaired students.

Collaborations with Major European Organizations

  • Partner 1: Univ. Freiburg, Brain State Decoding Laboratory (M. Tangermann), Germany

  • Topic 1: robust EEG spatial filters for single trial regression

  • Partner 2: TU Graz, Neural Engineering lab (R. Scherer), Austria

  • Topic 2: BCI pitfalls, negative results in BCI, guidelines for BCI design

  • Partner 3: EPFL, Defitech Foundation Chair in Brain-machine Interface (R. Chavarriaga), Switzerland

  • Topic 3: BCI pitfalls, negative results in BCI

  • Partner 4: Oldenbourg University, Neuropsychology department (S. Debener, C. Zich), Germany

  • Topic 4: guidelines for BCI design

  • Partner: Twente University (A. Nijholt), Enschede, The Netherlands

  • Topic: Handbook of Brain-Computer Interfaces