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When performing studies on brain computer interface based rehabilitation problems, researchers frequently encounter difficulty due to the curse of dimensionality and the nonlinear nature of Motor Imagery Electroencephalography (MI-EEG). Though many approaches have been proposed recently to address the feature extraction problem and have shown surprising performance, unfortunately, most of them are...
This paper develops a manifold-oriented stochastic neighbor projection (MSNP) technique for feature extraction. MSNP is designed to find a linear projection for the purpose of capturing the underlying pattern structure of observations that actually lie on a nonlinear manifold. In MSNP, the similarity information of observations is encoded with stochastic neighbor distribution based on geodesic distance...
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