Generating Artificial EEG Signals To Reduce BCI Calibration Time - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2011

Generating Artificial EEG Signals To Reduce BCI Calibration Time

Résumé

One of the major limitations of Brain-Computer Interfaces (BCI) is their long calibration time. This is due to the need to collect numerous training EEG trials for the machine learning algorithm used in their design. In this paper we propose a new approach to reduce this calibration time. This approach consists in generating arti ficial EEG trials from the few EEG trials initially available, in order to augment the training set size in a relevant way. The approach followed is simple and computationally efficient. Moreover, our offline evaluations suggested that it can lead to signi ficant increases in classification accuracy when compared with existing approaches, especially when the number of training trials available is small. As such, it can indeed be used to reduce calibration time.
Fichier principal
Vignette du fichier
bciworkshop2011.pdf (258.58 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

inria-00599325 , version 1 (09-06-2011)

Identifiants

  • HAL Id : inria-00599325 , version 1

Citer

Fabien Lotte. Generating Artificial EEG Signals To Reduce BCI Calibration Time. 5th International Brain-Computer Interface Workshop, Sep 2011, Graz, Austria. pp.176-179. ⟨inria-00599325⟩
665 Consultations
1502 Téléchargements

Partager

Gmail Facebook X LinkedIn More