Engineering Sciences

Yazarlar: Ahmet Alkan

Konular:-

DOI:10.12739/nwsaes.v6i1.5000067050

Anahtar Kelimeler:EEG,Epilepsy,LDA,SVM,K-Means

Özet: In this study, normal and epileptic EEG signals are analyzed by using different preprocessing, classification/clustering methods and results are compared. Mean Absolute values and parametric models such as Yule-Walker AR and Covariance methods are used fort he feature extraction. For the classification of EEG signals Linear Discriminant Analysis, Support Vector Machine (SVM) methods are used. Clustering techniques such as K-means and Fuzzy C-means are also used for the analysis of the EEG signals. The comparative results confirmed that the proposed methods achieved high classification rates.

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