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Spatially Regularized Common Spatial Patterns for EEG Classification

Abstract : In this paper, we propose a new algorithm for Brain- Computer Interface (BCI): the Spatially Regularized Common Spatial Patterns (SRCSP). SRCSP is an extension of the famous CSP algorithm which includes spatial a priori in the learning process, by adding a regularization term which penalizes spatially non smooth filters. We compared SRCSP and CSP algorithms on data of 14 subjects from BCI competitions. Results suggested that SRCSP can improve performances, around 10% more in classification accuracy, for subjects with poor CSP performances. They also suggested that SRCSP leads to more physiologically relevant filters than CSP
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Contributor : Fabien Lotte Connect in order to contact the contributor
Submitted on : Thursday, April 29, 2010 - 1:15:50 PM
Last modification on : Thursday, May 9, 2019 - 4:16:06 PM
Long-term archiving on: : Thursday, September 23, 2010 - 6:04:35 PM


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  • HAL Id : inria-00447435, version 3


Fabien Lotte, Cuntai Guan. Spatially Regularized Common Spatial Patterns for EEG Classification. International Conference on Pattern Recognition (ICPR), Aug 2010, Istanbul, Turkey. pp.3712-3715. ⟨inria-00447435v3⟩



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