Bayesian model of individual learning to control a motor imagery BCI - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

Bayesian model of individual learning to control a motor imagery BCI

Résumé

The cognitive mechanisms underlying subjects' self-regulation in Brain-Computer Interface (BCI) and neurofeedback (NF) training remain poorly understood. Yet, a mechanistic computational model of each individual learning trajectory is required to improve the reliability of BCI applications. The few existing attempts mostly rely on model-free (reinforcement learning) approaches. Hence, they cannot capture the strategy developed by each subject and neither finely predict their learning curve. In this study, we propose an alternative, model-based approach rooted in cognitive skill learning within the Active Inference framework. We show how BCI training may be framed as an inference problem under high uncertainties. We illustrate the proposed approach on a previously published synthetic Motor Imagery ERD laterality training. We show how simple changes in model parameters allow us to qualitatively match experimental results and account for various subject. In the near future, this approach may provide a powerful computational to model individual skill learning and thus optimize and finely characterize BCI training.
Fichier principal
Vignette du fichier
submission_annicchiarico_lotte_mattout_final.pdf (632.07 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04725503 , version 1 (08-10-2024)

Identifiants

Citer

Côme Annicchiarico, Fabien Lotte, Jérémie Mattout. Bayesian model of individual learning to control a motor imagery BCI. 9th International Graz Brain-Computer Interface Conference 2024, Sep 2024, Graz, Austria. ⟨10.3217/978-3-99161-014-4-083⟩. ⟨hal-04725503⟩
0 Consultations
0 Téléchargements

Altmetric

Partager

More