Classifying Regularized Sensor Covariance Matrices: An Alternative to CSP
IEEE Transactions on Neural Systems and Rehabilitation Engineering(2016)
摘要
Common spatial patterns (CSP) is a commonly used technique for classifying imagined movement type brain-computer interface (BCI) datasets. It has been very successful with many extensions and improvements on the basic technique. However, a drawback of CSP is that the signal processing pipeline contains two supervised learning stages: the first in which class-relevant spatial filters are learned an...
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关键词
Covariance matrices,Eigenvalues and eigenfunctions,Noise,Supervised learning,Standards,Robustness,Logistics
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