Bilinear Regularized Locality Preserving Learning on Riemannian Graph for Motor Imagery BCI.
IEEE Transactions on Neural Systems and Rehabilitation Engineering(2018)
摘要
In off-line training of motor imagery-based brain-computer interfaces (BCIs), to enhance the generalization performance of the learned classifier, the local information contained in test data could be used to improve the performance of motor imagery as well. Further considering that the covariance matrices of electroencephalogram (EEG) signal lie on Riemannian manifold, in this paper, we construct...
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关键词
Manifolds,Covariance matrices,Electroencephalography,Training,Feature extraction,Support vector machines,Symmetric matrices
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