Generating Artificial EEG Signals To Reduce BCI Calibration Time

Fabien Lotte 1, 2, *
* Auteur correspondant
1 IPARLA - Visualization and manipulation of complex data on wireless mobile devices
Université Sciences et Technologies - Bordeaux 1, Inria Bordeaux - Sud-Ouest, École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), CNRS - Centre National de la Recherche Scientifique : UMR5800
Abstract : 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.
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https://hal.inria.fr/inria-00599325
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Fabien Lotte. Generating Artificial EEG Signals To Reduce BCI Calibration Time. 5th International Brain-Computer Interface Workshop, Sep 2011, Graz, Austria. pp.176-179, 2011. 〈inria-00599325〉

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