Generating Artificial EEG Signals To Reduce BCI Calibration TimeReport as inadecuate




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* Corresponding author 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 2 LaBRI - Laboratoire Bordelais de Recherche en Informatique

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.

keyword : Brain-Computer Interfaces calibration time reduction artificial data generation





Author: Fabien Lotte -

Source: https://hal.archives-ouvertes.fr/



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