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
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 i...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | Informatics in Medicine Unlocked |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S235291482100201X |
| _version_ | 1819003835729838080 |
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| author | Laurent Chanel Djoufack Nkengfack Daniel Tchiotsop Romain Atangana Beaudelaire Saha Tchinda Valérie Louis-Door Didier Wolf |
| author_facet | Laurent Chanel Djoufack Nkengfack Daniel Tchiotsop Romain Atangana Beaudelaire Saha Tchinda Valérie Louis-Door Didier Wolf |
| author_sort | Laurent Chanel Djoufack Nkengfack |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T23:27:19Z |
| format | Article |
| id | doaj.art-bf7f877536d4432bbf5aa40bdb6b6459 |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-12-20T23:27:19Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-bf7f877536d4432bbf5aa40bdb6b64592022-12-21T19:23:22ZengElsevierInformatics in Medicine Unlocked2352-91482021-01-0126100721A comparison study of polynomial-based PCA, KPCA, LDA and GDA feature extraction methods for epileptic and eye states EEG signals detection using kernel machinesLaurent Chanel Djoufack Nkengfack0Daniel Tchiotsop1Romain Atangana2Beaudelaire Saha Tchinda3Valérie Louis-Door4Didier Wolf5Unité 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; 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; Corresponding author. Faculty of Science, University of Dschang-Cameroon, P.O. Box 67, Dschang.Unité de Recherche d'Automatique et d’Informatique Appliquée (UR-AIA), IUT-FV of Bandjoun, University of Dschang-Cameroon, P.O. Box 134, Bandjoun, CameroonDivision of Continuing Education and Distance Learning, Higher Teacher Training College (HTTC) of Bertoua, University of Ngaoundéré-Cameroon, P.O. Box 652, Bertoua, CameroonUnité de Recherche d'Automatique et d’Informatique Appliquée (UR-AIA), IUT-FV of Bandjoun, University of Dschang-Cameroon, P.O. Box 134, Bandjoun, CameroonCentre de Recherche en Automatique de Nancy (CRAN), UMR CNRS 7039, ENSEM de Lorraine, Nancy, FranceCentre de Recherche en Automatique de Nancy (CRAN), UMR CNRS 7039, ENSEM de Lorraine, Nancy, FranceBackground 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.http://www.sciencedirect.com/science/article/pii/S235291482100201XPolynomial transformsFeature extraction methodsLow-dimensional featuresKernel machinesEpileptic and eye states electroencephalogram (EEG) signals detection |
| spellingShingle | Laurent Chanel Djoufack Nkengfack Daniel Tchiotsop Romain Atangana Beaudelaire Saha Tchinda Valérie Louis-Door Didier Wolf 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 Polynomial transforms Feature extraction methods Low-dimensional features Kernel machines Epileptic and eye states electroencephalogram (EEG) signals detection |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | comparison study of polynomial based pca kpca lda and gda feature extraction methods for epileptic and eye states eeg signals detection using kernel machines |
| topic | Polynomial transforms Feature extraction methods Low-dimensional features Kernel machines Epileptic and eye states electroencephalogram (EEG) signals detection |
| url | http://www.sciencedirect.com/science/article/pii/S235291482100201X |
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