Learning from other Subjects Helps Reducing Brain-Computer Interface Calibration Time

Abstract : A major limitation of Brain-Computer Interfaces (BCI) is their long calibration time, as much data from the user must be collected in order to tune the BCI for this target user. In this paper, we propose a new method to reduce this calibration time by using data from other subjects. More precisely, we propose an algorithm to regularize the Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) algorithms based on the data from a subset of automatically selected subjects. An evaluation of our approach showed that our method significantly outperformed the standard BCI design especially when the amount of data from the target user is small. Thus, our approach helps in reducing the amount of data needed to achieve a given performance level.
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Communication dans un congrès
International Conference on Audio Speech and Signal Processing (ICASSP), Mar 2010, Dallas, United States. pp.614-617, 2010
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https://hal.inria.fr/inria-00441670
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Dernière modification le : lundi 16 juillet 2012 - 18:33:46
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  • HAL Id : inria-00441670, version 1

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Fabien Lotte, Cuntai Guan. Learning from other Subjects Helps Reducing Brain-Computer Interface Calibration Time. International Conference on Audio Speech and Signal Processing (ICASSP), Mar 2010, Dallas, United States. pp.614-617, 2010. 〈inria-00441670〉

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