Generating Artificial EEG Signals To Reduce BCI Calibration Time
LOTTE, Fabien
Visualization and manipulation of complex data on wireless mobile devices [IPARLA ]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Visualization and manipulation of complex data on wireless mobile devices [IPARLA ]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
LOTTE, Fabien
Visualization and manipulation of complex data on wireless mobile devices [IPARLA ]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
< Reduce
Visualization and manipulation of complex data on wireless mobile devices [IPARLA ]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Language
en
Communication dans un congrès
This item was published in
5th International Brain-Computer Interface Workshop, 2011-09, Graz. 2011-09p. 176-179
English 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 ...Read more >
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.Read less <
Italian Keywords
Brain-Computer Interfaces
calibration time reduction
artificial data generation
Origin
Hal imported