Minimizing subject-dependent calibration for BCI with Riemannian transfer learning
This work addresses the issue of cognitive fatigue for BCI users by reducing calibration time, though it appears incremental as it builds on existing Riemannian BCI techniques.
The paper tackled the problem of lengthy subject-dependent calibration in Brain-Computer Interfaces (BCI) by proposing a Riemannian transfer learning scheme, which significantly improved classifier reliability across multiple BCI paradigms.
Calibration is still an important issue for user experience in Brain-Computer Interfaces (BCI). Common experimental designs often involve a lengthy training period that raises the cognitive fatigue, before even starting to use the BCI. Reducing or suppressing this subject-dependent calibration is possible by relying on advanced machine learning techniques, such as transfer learning. Building on Riemannian BCI, we present a simple and effective scheme to train a classifier on data recorded from different subjects, to reduce the calibration while preserving good performances. The main novelty of this paper is to propose a unique approach that could be applied on very different paradigms. To demonstrate the robustness of this approach, we conducted a meta-analysis on multiple datasets for three BCI paradigms: event-related potentials (P300), motor imagery and SSVEP. Relying on the MOABB open source framework to ensure the reproducibility of the experiments and the statistical analysis, the results clearly show that the proposed approach could be applied on any kind of BCI paradigm and in most of the cases to significantly improve the classifier reliability. We point out some key features to further improve transfer learning methods.