Shaymaaadnan Abdulrahman,Mohamed Roushdy,Abdel-Badeeh M. Salem,



Electroencephalogram (EEG),Support vector machine,K-Nearest Neighbor,Machine learning,


The signals of the electroencephalogram (EEG) have been applied for detecting as well as registering the electrical efficiency in the human brain.  In this paper, EEG signals have been utilized for human identification. The reliability regarding a lot of biometric systems aren’t adequate due to the possibility of being copied or faked. Thus the brain signatures have been applied as potential biometric identifiers. The aim of this paper is to apply sample entropy and graph entropy as feature extraction. While in classification Support vector machine (SVM) and K-Nearest Neighbor (KNN) have achieved. Machine Learning Repository (UCI) used as dataset. Experimental consequences on this dataset demonstrate substantial enhancement in the classification accuracy as compared with other testified results in the literature. Results showed that the classification accuracy with SVM for biometric identification is 90.8% while with K-NN is 83.7% .Our study using13channels to feature extraction.


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