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Modified mixture of experts employing eigenvector methods and Lyapunov exponents for analysis of electroencephalogram signals
Author(s) -
Übeyli Elif Derya
Publication year - 2009
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2009.00490.x
Subject(s) - computer science , pattern recognition (psychology) , spectral density , electroencephalography , eigenvalues and eigenvectors , artificial intelligence , artificial neural network , lyapunov exponent , speech recognition , psychology , telecommunications , quantum mechanics , psychiatry , chaotic , physics
The use of diverse features in detecting variability of electroencephalogram (EEG) signals is presented. The classification accuracies of the modified mixture of experts (MME), which was trained on diverse features, were obtained. Eigenvector methods (Pisarenko, multiple signal classification – MUSIC, and minimum‐norm) were selected to generate the power spectral density estimates. The features from the power spectral density estimates 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 the diverse features achieved high accuracy rates (total classification accuracy of the MME is 98.33%).