Transfer learning for the Riemannian tangent space: Applications to Brain-Computer Interfaces - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Transfer learning for the Riemannian tangent space: Applications to Brain-Computer Interfaces

Résumé

Transfer learning for training brain-computer interface (BCI) decoding algorithms is useful to reduce the calibration time, increase the accuracy, reduce the risk of overfitting and allow the application of machine learning methods that require a large amount of data, such as deep neuronal networks. In this article we propose a transfer learning method inspired by recent advances in Riemannian geometry. The method aligns vectors in the tangent space of a source and a target data set by means of Procrustes Analysis. We apply the method on a publicly available P300-BCI database. We show that using our method it is possible to transfer information reusing data from other subjects. The classification accuracy we obtain, as compared to the state of art, shows a clear transmission of information using the transfer learning method.
Fichier principal
Vignette du fichier
Tangent_Space_Alignment.pdf (556.3 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03420920 , version 1 (09-11-2021)

Identifiants

Citer

Alexandre Bleuzé, Jérémie Mattout, Marco Congedo. Transfer learning for the Riemannian tangent space: Applications to Brain-Computer Interfaces. ICEET 2021 - 2021 International Conference on Engineering and Emerging Technologies, Oct 2021, Istanbul, Turkey. pp.1-6, ⟨10.1109/ICEET53442.2021.9659607⟩. ⟨hal-03420920⟩
171 Consultations
362 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More