Please use this identifier to cite or link to this item:
Title: Features extracted by eigenvector methods for detecting variability of EEG signals
Authors: Übeyli, Elif Derya
Güler, İnan
Keywords: mixture of experts (ME)
modified mixture of experts (MME)
eigenvector methods
electroencephalogram (EEG) signals
Issue Date: 2007
Publisher: Elsevier
Abstract: 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. (c) 2006 Elsevier B.V. All rights reserved.
ISSN: 0167-8655
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender


checked on Sep 23, 2022


checked on Sep 24, 2022

Page view(s)

checked on Dec 26, 2022

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.