Modified Mixture of Experts for Analysis of EEG Signals

In this paper, the usage of diverse features in detecting variability of electroencephalogram (EEG) signals was presented. The classification accuracies of modified mixture of experts (MME), which were trained on diverse features, were obtained. The wavelet coefficients and Lyapunov exponents of the...

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Bibliographic Details
Main Author: Ubeyli, E.D.
Format: Conference Proceeding
Language:English
Subjects:
Brain modeling
Computer vision
Distributed computing
Diverse features
Electroencephalography
Epilepsy
Jacobian matrices
Lyapunov exponents
Modified mixture of experts
Neural networks
Pattern classification
Signal analysis
Wavelet coefficients
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Summary:In this paper, the usage of diverse features in detecting variability of electroencephalogram (EEG) signals was presented. The classification accuracies of modified mixture of experts (MME), which were trained on diverse features, were obtained. The wavelet coefficients and Lyapunov exponents of the EEG signals were computed and statistical features were calculated to depict their distribution. The statistical features, which were used for obtaining the diverse features of the EEG signals, were then input into the implemented neural network models for training and testing purposes. The present study demonstrated that the MME trained on diverse features achieved high accuracy rates.
ISSN:1094-687X
1558-4615
DOI:10.1109/IEMBS.2007.4352598