학술논문

Minimizing Subject-dependent Calibration for BCI with Riemannian Transfer Learning
Document Type
Conference
Source
2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Neural Engineering (NER), 2021 10th International IEEE/EMBS Conference on. :523-526 May, 2021
Subject
Bioengineering
Signal Processing and Analysis
Training
Statistical analysis
Transfer learning
Pipelines
Neural engineering
Reliability engineering
User experience
Language
ISSN
1948-3554
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: motor imagery, event-related potentials (P300) 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.