Multi-Class Independent Common Spatial Patterns: Exploiting Energy Variations of Brain Sources

Abstract : This paper presents a method to recover task-related sources from a multi-class Brain-Computer Interface (BCI) based on motor imagery. Our method gathers two common approaches to tackle the multi-class problem: 1) the supervised approach of Common Spatial Pattern (CSP) to discriminate between different tasks; 2) the criterion of statistical independence of non-stationary sources used in Independent Component Analysis (ICA). We show that the resulting spatial filters have to be adapted to each subject and that the combined use of intra-trial and inter-class energy variations of brain sources yield an increase of classification rates for four among eight sub jects.
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https://hal.archives-ouvertes.fr/hal-00323579
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Submitted on : Monday, September 22, 2008 - 2:55:09 PM
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  • HAL Id : hal-00323579, version 1

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Cedric Gouy-Pailler, Marco Congedo, Clemens Brunner, Christian Jutten, Gert Pfurtscheller. Multi-Class Independent Common Spatial Patterns: Exploiting Energy Variations of Brain Sources. Brain-Computer Interface Workshop and Training Course 2008, Sep 2008, Graz, Austria. pp.20--25. ⟨hal-00323579⟩

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