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

Cedric Gouy-Pailler (1) , Marco Congedo (1) , Clemens Brunner (2) , Christian Jutten (1) , Gert Pfurtscheller (2)

1 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
2 GRAZ BCI - Laboratory of Brain-Computer Interfaces

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.

Domaines: Biotechnologies; Biotechnologie; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Cedric Gouy-Pailler, Marco Congedo, Clemens Brunner, Christian Jutten, Gert Pfurtscheller. Multi-Class Independent Common Spatial Patterns: Exploiting Energy Variations of Brain Sources. BCI 2008 - 4th International Brain-Computer Interface Workshop and Training Course, Sep 2008, Graz, Austria. pp.20--25. ⟨hal-00323579⟩