An Efficient P300-based Brain-Computer Interface with Minimal Calibration Time

Abstract : In this paper we propose a new design for P300-based BCI, in order to reduce the calibration time of the system. Our BCI is based on Regularized Canonical Correlation Analysis for feature extraction and Regularized Linear Discriminant Analysis for classification. Evaluations suggested that this design can reach good P300 detection performances while using much less training examples than current approaches, hence effectively reducing the calibration time.
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Communication dans un congrès
Assistive Machine Learning for People with Disabilities symposium (NIPS'09 Symposium), Dec 2009, Vancouver, Canada. 2009
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https://hal.inria.fr/inria-00430563
Contributeur : Fabien Lotte <>
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Dernière modification le : lundi 9 novembre 2009 - 08:40:45
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  • HAL Id : inria-00430563, version 1

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Fabien Lotte, Cuntai Guan. An Efficient P300-based Brain-Computer Interface with Minimal Calibration Time. Assistive Machine Learning for People with Disabilities symposium (NIPS'09 Symposium), Dec 2009, Vancouver, Canada. 2009. 〈inria-00430563〉

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