Towards Explanatory Feedback for User Training in Brain–Computer Interfaces

Julia Schumacher (1, 2) , Camille Jeunet (3, 2) , Fabien Lotte (4, 2)

1 Bernstein Center for Computational Neuroscience [Berlin]
2 Potioc - Popular interaction with 3d content
3 UB - Université de Bordeaux
4 LaBRI - Laboratoire Bordelais de Recherche en Informatique

Despite their potential for many applications, Brain-Computer Interfaces (BCI) are still rarely used due to their low reliability and long training. These limitations are partly due to inappropriate training protocols, which includes the feedback provided to the user. While feedback should theoretically be explanatory, motivating and meaningful, current BCI feedback is usually boring, corrective only and difficult to understand. In this study, different features of the electroencephalogram signals were explored to be used as a richer, explanatory BCI feedback. First, based on offline mental imagery BCI data, muscular relaxation was notably found to be negatively correlated to BCI performance. Second, this study reports on an online BCI evaluation using muscular relaxation as additional feedback. While this additional feedback did not lead to significant change in BCI performance, this study showed that multiple feedbacks can be used without deteriorating performance and provided interesting insights for explanatory BCI feedback design.

Mots clés: Brain-Computer Interfaces; Training; Feedback

Domaines: Apprentissage [cs.LG]; Intelligence artificielle [cs.AI]; Environnements Informatiques pour l'Apprentissage Humain; Traitement du signal et de l'image [eess.SP]; Sciences cognitives

Julia Schumacher, Camille Jeunet, Fabien Lotte. Towards Explanatory Feedback for User Training in Brain–Computer Interfaces. IEEE International Conference on Systems Man & Cybernetics (IEEE SMC), Oct 2015, Hong-Kong, China. ⟨hal-01179329⟩