Learning How to Generate Kinesthetic Motor Imagery Using a BCI-based Learning Environment: a Comparative Study Based on Guided or Trial-and-Error Approaches

Sébastien Rimbert (1, 2, 3) , Laurent Bougrain (1, 3) , Stéphanie Fleck (2)

1 NEUROSYS - Analysis and modeling of neural systems by a system neuroscience approach
2 PErSEUs - Psychologie Ergonomique et Sociale pour l'Expérience utilisateurs
3 LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications

Kinesthetic Motor Imagery (KMI) is a mental task which, if performed properly, can be very relevant in sports training or rehabilitation with a Brain-Computer Interface (BCI). Unfortunately, this mental task is generally complex to perform and can lead to a high degree of variability in its execution, reducing its potential benefits. The reason why the task of KMI is so difficult to perform is because there is no standardized way of instructing the subject in this mental task. This study presents an innovative BCI called Grasp-IT thought to support the learning of the KMI task, and the evaluation of two different learning methods: (i) a first one guided by an experimenter and based on the notion of progressiveness and (ii) a second one where the learners are alone and practice by trial and error. Our findings based on EEG analyses and subjective questionnaires validate the design of the Grasp-IT BCI and opens up perspectives on KMI learning modalities.

Mots clés: Kinesthetic Motor Imagery; Brain-Computer Interface; Grasp-IT; Stroke Rehabilitation; BCI learning environ- ment; Human-Computer Interaction

Domaines: Informatique [cs]; Environnements Informatiques pour l'Apprentissage Humain; Interface homme-machine [cs.HC]; Apprentissage [cs.LG]; Neurosciences; Neurosciences [q-bio.NC]; Machine Learning [stat.ML]

Sébastien Rimbert, Laurent Bougrain, Stéphanie Fleck. Learning How to Generate Kinesthetic Motor Imagery Using a BCI-based Learning Environment: a Comparative Study Based on Guided or Trial-and-Error Approaches. SMC 2020 - International IEEE Conference on Systems, Man and Cybernetics, Oct 2020, Toronto, Canada. ⟨hal-02920306⟩