Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Elements of Wavelets for Engineers and Scientists
Elements of Wavelets for Engineers and Scientists
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Wavelet packet-based feature extraction for brain-computer interfaces
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Multi-modal biometric emotion recognition using classifier ensembles
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
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Given their multiresolution temporal and spectral locality, wavelets are powerful candidates for decomposition, feature extraction, and classification of non-stationary electroencephalographic (EEG) signals for brain-computer interface (BCI) applications. Wavelet feature extraction methods offer several options through the choice of wavelet families and decomposition architectures. The classification results of EEG signals generated from imagined motor, cognitive, and affective tasks are presented using support vector machine (SVM) classifiers, indicating that these methods are suitable for imagined motor, cognitive and affective classification. Classifier performances of better than 80% for six imagined motor tasks, and for two affective tasks were achieved. Three cognitive tasks were successfully classified with 70% accuracy. The methods can be used with a variety of EEG signal reference methods and electrode placement locations. Wavelet features performed satisfactorily in the presence of noise when the classifiers were presented with contaminated training data.