Clustered Common Spatial Patterns
نویسندگان
چکیده
We propose to cluster class-wise covariance matrices in order to identify different groups of covariances contributing to the same condition. Each cluster represents a different brain pattern associated with one class. Further, we present Clustered Common Spatial Patterns, a new algorithm that applies this technique prior to CSP. We show that CCSP can outperform CSP in a binary imagery movement task. Although in this work we consider only the case of CSP, this clustering technique could also be used to improve other feature extraction methods.
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