The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
An experimental evaluation of ensemble methods for EEG signal classification
Pattern Recognition Letters
The random electrode selection ensemble for EEG signal classification
Pattern Recognition
The random electrode selection ensemble for EEG signal classification
Pattern Recognition
The Extreme Energy Ratio Criterion for EEG Feature Extraction
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Extreme energy difference for feature extraction of EEG signals
Expert Systems with Applications: An International Journal
A subject transfer framework for EEG classification
Neurocomputing
EEG signal classification using the event-related coherence and genetic algorithm
BICS'13 Proceedings of the 6th international conference on Advances in Brain Inspired Cognitive Systems
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An optimal nonlinear feature extractor for extracting energy features under two different kinds of patterns is proposed. It carries out the simultaneous diagonalization of two signal covariance matrices in a high-dimensional kernel transformed space, and thus promises to find features which are more discriminant, especially when the original data have nonlinear structures. Two operations, whitening transform and projection transform, are involved in kernel spaces. The mechanism of the feature extractor and its effectivity are shown with simulation data and the classification task of real electroencephalographic (EEG) signals.