Comparative Study
doi: 10.1109/TNSRE.2015.2477687.
Epub 2015 Sep 10.
Classifying Regularized Sensor Covariance Matrices: An Alternative to CSP
- PMID: 26372428
- DOI: 10.1109/TNSRE.2015.2477687
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Comparative Study
Classifying Regularized Sensor Covariance Matrices: An Alternative to CSP
IEEE Trans Neural Syst Rehabil Eng.
2016 Aug.
Abstract
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 and a second in which a classifier is used to classify the filtered variances. This may lead to potential overfitting issues, which are generally avoided by limiting CSP to only a few filters.
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