Classifying EEG for Brain Computer Interfaces Using Gaussian Process

Mingjun Zhong 1 Fabien Lotte 1 Mark Girolami 2 Anatole Lécuyer 1
1 BUNRAKU - Perception, decision and action of real and virtual humans in virtual environments and impact on real environments
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, ENS Cachan - École normale supérieure - Cachan, Inria Rennes – Bretagne Atlantique
Abstract : Classifying electroencephalography (EEG) signals is an important step for proceeding EEG-based brain computer interfaces (BCI). Currently, kernel based methods such as sup- port vector machine (SVM) are the state-of-the-art methods for this problem. In this paper, we apply Gaussian process (GP) classfi¯cation to binary classi¯cation problems of motor imaging EEG data. Comparing with SVM, GP based method naturally provides a predic- tive probability for identifying a trusted prediction which can be used for post-processing in a BCI. Experimental results show that the classification methods based on Gaussian process outperform SVM and K-Nearest Neighbor (KNN) in terms of 0-1 loss class prediction error.
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Contributor : Fabien Lotte <>
Submitted on : Monday, March 26, 2007 - 5:53:36 PM
Last modification on : Thursday, May 9, 2019 - 4:16:10 PM


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  • HAL Id : inria-00134966, version 2


Mingjun Zhong, Fabien Lotte, Mark Girolami, Anatole Lécuyer. Classifying EEG for Brain Computer Interfaces Using Gaussian Process. Pattern Recognition Letters, Elsevier, 2007. ⟨inria-00134966v2⟩



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