Kernel learning over the manifold of symmetric positive definite matrices for dimensionality reduction in a BCI application

Author:

Sadatnejad Khadijeh,Shiry Ghidary Saeed

Funder

Institute for Cognitive Sciences and Technologies Council

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference50 articles.

1. Improving support vector machine classifiers by modifying kernel functions;Amari;Neural Netw.,1999

2. Riemannian geometry applied to BCI classification;Barachant,2010

3. Multiclass brain–computer interface classification by Riemannian geometry;Barachant;IEEE Trans. Biomed. Eng.,2012

4. A. Barachant, S. Bonnet, M. Congedo, C. Jutten, BCI signal classification using a Riemannian-based kernel, in: Proceedings of the 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), Michel Verleysen, April 2012, pp. 97–102.

5. Classification of covariance matrices using a Riemannian-based kernel for BCI applications;Barachant;Neurocomputing,2013

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