Classification of Brain Signals in Normal Subjects and Patients with Epilepsy Using Mixture of Experts

EEG is one of the most important and common sources for study of brain function and neurological disorders. Automated systems are under study for many years to detect EEG changes. Because of the importance of making correct decision، we are looking for better classification methods for EEG signals. In this paper a smart compound system is used for classifying EEG signals to different groups. Since in each classification the system accuracy of making decision is very important، in this study we look for some methods to improve the accuracy of EEG signals classification. In this paper the use of Mixture of Experts for improving the EEG signals classification of normal subjects and patients with epilepsy is shown and the classification accuracy is evaluated. Decision making was performed in two stages: 1) feature extractions with different methods of eigenvector and 2) Classification using the classifier trained by extracted features. This smart system inputs are formed from composites features that are selected appropriate with network structure. In this study tree methods based on eigenvectors (Minimum Norm، MUSIC، Pisarenko) are chosen for the estimation of Power Spectral Density (PSD). After the implementation of ME and train it on composite features، we propose that this technique can reach high classification accuracy. Hence، EEG signals classification of epilepsy patients in different situations and control subjects is available. In this study، Mixture of Experts structure was used for EEG signals classification. Proper performance of Neural Network depends on the size of train and test data. Combination of multiple Neural Networks even without using the probable structure in obtaining weights in classification problem can produce high accuracy in less time، which is important and valuable in the classification point of view.