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Conference Papers Year : 2017

Towards a cognitive model of MI-BCI user training

Abstract

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) enable users to control applications using their brain activity alone, by realising mental-imagery tasks. Although promising, MI-BCIs remain barely used outside laboratories, notably due to the difficulties users encounter when attempting to control them. We claim that understanding and improving the user-training process could greatly improve users' MI-BCI control abilities. Yet, to better understand the training process, we need a model of the factors impacting MI-BCI performance. In other words, we need to understand which traits and states impact MI-BCI performance, how these factors interact and how to influence them to improve this performance. Such a model would enable us to design adapted and adaptive training protocols, to guide neurophysiological analyses or design informed classi-fiers, among others. In this paper we propose a theoretical model of MI-BCI tasks, which is the first step towards the design of this full cognitive and computational model.
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Dates and versions

hal-01519476 , version 1 (15-05-2017)

Identifiers

  • HAL Id : hal-01519476 , version 1

Cite

Camille Jeunet, Bernard N'Kaoua, Fabien Lotte. Towards a cognitive model of MI-BCI user training . International Graz BCI Conference , Sep 2017, Graz, Austria. ⟨hal-01519476⟩
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