Classification of covariance matrices using a Riemannian-based kernel for BCI applications

NEUROCOMPUTING

卷:112

Article

Barachant, Alexandre^1,2  Bonnet, Stephane¹  Congedo, Marco²  Jutten, Christian² 

[1] CEA LETI, F-38054 Grenoble, France

[2] Grenoble Univ, CNRS, GIPSA Lab, Team ViBS Vis & Brain Signal Proc, F-38402 St Martin Dheres, France

关键词: Brain-computer interfaces;    Covariance matrix;    Kernel;    Support vector machine;    Riemannian geometry;   

DOI  :  10.1016/j.neucom.2012.12.039

来源: Elsevier

【 摘 要 】

The use of spatial covariance matrix as a feature is investigated for motor imagery EEG-based classification in brain-computer interface applications. A new kernel is derived by establishing a connection with the Riemannian geometry of symmetric positive definite matrices. Different kernels are tested, in combination with support vector machines, on a past BCI competition dataset. We demonstrate that this new approach outperforms significantly state of the art results, effectively replacing the traditional spatial filtering approach. (C) 2013 Elsevier B.V. All rights reserved.

【 授权许可】

Free