The nature of statistical learning theory
The nature of statistical learning theory
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An experimental evaluation of ensemble methods for EEG signal classification
Pattern Recognition Letters
The random electrode selection ensemble for EEG signal classification
Pattern Recognition
Measuring saliency of features representing EEG signals using signal-to-noise ratios
Expert Systems with Applications: An International Journal
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Spatial filter selection with LASSO for EEG classification
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Fuzzy Hopfield neural network clustering for single-trial motor imagery EEG classification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A subject transfer framework for EEG classification
Neurocomputing
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A new method called extreme energy difference (EED) is proposed for supervised feature extraction of electroencephalogram (EEG) signals. It is a linear feature extractor which aims at maximizing or minimizing the disparity of energy features between two classes of EEG signals. The final transform for feature extraction in EED is very concise, which is resolved by an eigenvalue decomposition problem. In the context of EEG signal classification for brain-computer interfaces, the performance of EED is evaluated with real EEG signals from different subjects. Experimental results on nine subjects show that the EED feature extractor is comparable to the state-of-the-art feature extraction method common spatial patterns (CSP). Furthermore on another benchmark data set, by combining features obtained by EED and CSP, we train a linear support vector machine classifier whose classification accuracy outperforms the best result reported. This shows EED can be a beneficial complement to CSP.