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

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...

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Bibliographic Details
Main Authors: Laurent Chanel Djoufack Nkengfack, Daniel Tchiotsop, Romain Atangana, Beaudelaire Saha Tchinda, Valérie Louis-Door, Didier Wolf
Format: Article
Language:English
Published: Elsevier 2021-01-01
Series:Informatics in Medicine Unlocked
Subjects:
Polynomial transforms
Feature extraction methods
Low-dimensional features
Kernel machines
Epileptic and eye states electroencephalogram (EEG) signals detection
Online Access:http://www.sciencedirect.com/science/article/pii/S235291482100201X

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