Minimizing subject-dependent calibration for BCI with Riemannian transfer learning

TL;DR

This paper introduces a transfer learning approach based on Riemannian geometry to reduce subject-dependent calibration in various BCI paradigms, demonstrating robustness and improved classifier reliability across multiple datasets.

Contribution

It presents a unified transfer learning scheme applicable to different BCI paradigms, enhancing calibration efficiency and classifier performance using Riemannian methods.

Findings

Applicable to P300, motor imagery, and SSVEP paradigms

Significantly improves classifier reliability in most cases

Validated on multiple datasets with open-source framework

Abstract

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.