A comparison study of polynomial-based PCA, KPCA, LDA and GDA feature extraction methods for epileptic and eye states EEG signals detection using kernel machines

Informatics in Medicine Unlocked

Romain Atangana¹  Daniel Tchiotsop²  Valérie Louis-Door³  Didier Wolf⁴  Beaudelaire Saha Tchinda⁴  Laurent Chanel Djoufack Nkengfack⁵ 

[1]Corresponding author. Faculty of Science, University of Dschang-Cameroon, P.O. Box 67, Dschang.

[2]Unité de Recherche d'Automatique et d’Informatique Appliquée (UR-AIA), IUT-FV of Bandjoun, University of Dschang-Cameroon, P.O. Box 134, Bandjoun, Cameroon

[3]Division of Continuing Education and Distance Learning, Higher Teacher Training College (HTTC) of Bertoua, University of Ngaoundéré-Cameroon, P.O. Box 652, Bertoua, Cameroon

[4]Unité de Recherche d'Automatique et d’Informatique Appliquée (UR-AIA), IUT-FV of Bandjoun, University of Dschang-Cameroon, P.O. Box 134, Bandjoun, Cameroon

[5]Unité de Recherche de Matière Condensée-d’Électronique et de Traitement Du Signal (UR-MACETS), Faculty of Science, University of Dschang-Cameroon, P.O. Box 67, Dschang, Cameroon

关键词: Polynomial transforms;    Feature extraction methods;    Low-dimensional features;    Kernel machines;    Epileptic and eye states electroencephalogram (EEG) signals detection;   

DOI  :  

来源: DOAJ

【 摘 要 】

Background and objective: Initially, analysis of Electroencephalogram (EEG) signals was purely visual, tedious, time-consuming, and required a physician. Changing this old approach to classification proves to be an extraordinary task that gained much attention and a great deal of effort. With this intention, this comparison study focused on the development of polynomial-based feature extraction methods for epileptic and eye states EEG signals detection using kernel machines. Method: Polynomial transforms are applied to decompose EEG signals in the frequency domain before their analysis using linear and non-linear measures. Thereafter, the standard and kernel extension methods are applied to determine principal components and discriminants which help to extract informative and discriminative low-dimensional features. For direct detection of EEG signals, extracted features are fed into kernel machines namely simple multilayer perceptron neural network (sMLPNN) and least-square support vector machine (LS-SVM). Results: Using the publicly available Bonn-University database, experimental results demonstrated that features extracted using kernel methods are more discriminative than the ones using standard methods. In addition, compared to the LS-SVM, polynomial-based features with sMLPNN gained higher performances. Moreover, obtained predictivity, accuracy, and area under receiver operating curve also demonstrate that kernel machines can detect epileptic and eye states EEG signals with highest performances of 100%, 100% and 1, respectively. Conclusion: Thus, the proposed framework can be efficient for EEG diagnosis. Overall, given the complexity and heterogeneity of the brain, it is likely frameworks of this type that will be required to configure intelligent devices for treating epilepsy and to configure eye-brain-computer interface.

【 授权许可】

Unknown