Bayesian model of individual learning to control a motor imagery BCI

TL;DR

This paper introduces a Bayesian, model-based approach using Active Inference to understand and predict individual learning trajectories in motor imagery BCI training, aiming to improve personalization and effectiveness.

Contribution

It presents a novel cognitive skill learning model within the Active Inference framework for BCI, capturing individual strategies and predicting learning curves more accurately.

Findings

Qualitatively matches experimental BCI training results

Accounts for variability across subjects

Provides a basis for optimizing BCI training protocols

Abstract

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.