Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Classification of EEG signals using the wavelet transform
Signal Processing
A connectionist method for pattern classification with diverse features
Pattern Recognition Letters
An improved neural classification network for the two-group problem
Computers and Operations Research
Adaptive mixtures of local experts
Neural Computation
Feature extraction from Doppler ultrasound signals for automated diagnostic systems
Computers in Biology and Medicine
A modified mixture of experts network structure for ECG beats classification with diverse features
Engineering Applications of Artificial Intelligence
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Statistics over features: EEG signals analysis
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Lyapunov exponents/probabilistic neural networks for analysis of EEG signals
Expert Systems with Applications: An International Journal
Epileptic seizure detection in EEGs using time-frequency analysis
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Computers in Biology and Medicine
Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection
EURASIP Journal on Advances in Signal Processing
Expert Systems with Applications: An International Journal
Time-frequency distributions in the classification of epilepsy from EEG signals
Expert Systems with Applications: An International Journal
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In this paper, we present the expert systems for detecting variability of electroencephalogram (EEG) signals. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Methods of combining multiple classifiers with diverse features are viewed as a general problem in various application areas of pattern recognition. Because of the importance of making the right decision, we are looking for better classification procedures for EEG signals. The mixture of experts (ME) and modified mixture of experts (MME) were tested and benchmarked for their performance on the classification of the studied EEG signals. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The inputs of these expert systems composed of diverse or composite features were chosen according to the network structures. The present study was conducted with the purpose of answering the question of whether the expert system with diverse features (MME) or composite feature (ME) improve the capability of classification of the EEG signals. Our research demonstrated that the MME trained on diverse features achieved accuracy rates which were higher than that of the ME.