The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
The Extreme Energy Ratio Criterion for EEG Feature Extraction
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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In the application of brain-computer interfaces (BCIs), energy features are both physiologically well-founded and empirically effective to describe electroencephalogram (EEG) signals for classifying brain activities. Recently, a linear method named extreme energy ratio (EER) for energy feature extraction of EEG signals in terms of spatial filtering was proposed. This paper gives a nonlinear extension of the linear EER method. Specifically, we use the kernel trick to derive a kernelized version of the original EER feature extractor. The solutions for optimizing the criterion in kernel EER are provided for future use.