Article

On classifiability of wavelet features for EEG-based brain-computer interfaces

Authors:

Jesse Sherwood,

Reza DerakhshaniAuthors Info & Claims

IJCNN'09: Proceedings of the 2009 international joint conference on Neural Networks

Pages 2508 - 2515

Published: 14 June 2009 Publication History

Abstract

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.

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Cited By

Kuncheva LChristy TPierce IMansoor S(2011)Multi-modal biometric emotion recognition using classifier ensemblesProceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I10.5555/2025756.2025796(317-326)Online publication date: 28-Jun-2011
https://dl.acm.org/doi/10.5555/2025756.2025796

On classifiability of wavelet features for EEG-based brain-computer interfaces
1. Computing methodologies
  1. Machine learning

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cover image Guide Proceedings

IJCNN'09: Proceedings of the 2009 international joint conference on Neural Networks

June 2009

3570 pages

ISBN:9781424435494

Sponsors

Georgia Tech: Georgia Institute of Technology
ieee-cis: IEEE Computational Intelligence Society
INNS: International Neural Network Society

Publisher

IEEE Press

Publication History

Published: 14 June 2009

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Cited By

Kuncheva LChristy TPierce IMansoor S(2011)Multi-modal biometric emotion recognition using classifier ensemblesProceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I10.5555/2025756.2025796(317-326)Online publication date: 28-Jun-2011
https://dl.acm.org/doi/10.5555/2025756.2025796

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