Features extracted by eigenvector methods for detecting variability of EEG signals

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.