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Towards Improved BCI based on Human Learning Principles

Fabien Lotte 1, 2 Camille Jeunet 1, 3
1 Potioc - Popular interaction with 3d content
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest
3 EA4136-Handicap Système nerveux
Université Bordeaux Segalen - Bordeaux 2
Abstract : Although EEG-based BCI are very promising for numerous applications, they mostly remain prototypes not used outside laboratories, due to their low reliability. Poor BCI performances are partly due to imperfect EEG signal processing algorithms but also to the user, who may not be able to produce reliable EEG patterns. This paper presents some of our current work that aims at addressing the latter, i.e., at guiding users to learn BCI control mastery. First, this paper identifies some theoretical (based on human learning psychology models) and practical limitations of current standard BCI training approaches and thus the need for alternative ones. To try to address these limitations, we conducted a study to explore what kind of users can use a BCI and why, and will present the main results. We also present new feedback types we designed to help users to learn BCI control skills more efficiently.
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Submitted on : Saturday, January 31, 2015 - 5:45:35 PM
Last modification on : Monday, December 20, 2021 - 4:50:14 PM
Long-term archiving on: : Friday, May 1, 2015 - 10:20:11 AM


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  • HAL Id : hal-01111843, version 1



Fabien Lotte, Camille Jeunet. Towards Improved BCI based on Human Learning Principles. 3rd International Winter Conference on Brain-Computer Interfaces, Jan 2015, High1 Resort, South Korea. ⟨hal-01111843⟩



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