Investigating the impacts of epilepsy on EEG-based person identification systems

Dinh Phung, Dat TRAN, Wanli MA, Phuoc Nguyen, Tien Pham

Research output: A Conference proceeding or a Chapter in BookConference contribution

4 Citations (Scopus)
3 Downloads (Pure)

Abstract

Person identification using electroencephalogram (EEG) as biometric has been widely used since it is capable of achieving high identification rate. Epilepsy is one of the brain disorders that involves in the EEG signal and hence it may have impact on EEG-based person identification systems. However, this issue has not been investigated. In this paper, we perform person identification on two groups of subjects, normal and epileptic to investigate the impact of epilepsy on the identification rate. Autoregressive model (AR) and Approximate entropy (ApEn) are employed to extract features from these two groups. Experimental results show that epilepsy actually have impacts depending on feature extraction method used in the system.
Original languageEnglish
Title of host publication2014 International Joint Conference on Neural Networks (IJCNN)
EditorsDerong Liu, Jennie Si
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages3644-3648
Number of pages5
ISBN (Electronic)9781479914845
ISBN (Print)9781479966271, 9781479914821
DOIs
Publication statusPublished - 6 Jul 2014
EventIEEE World Congress on Computational Intelligence 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

ConferenceIEEE World Congress on Computational Intelligence 2014
CountryChina
CityBeijing
Period6/07/1411/07/14

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    Phung, D., TRAN, D., MA, W., Nguyen, P., & Pham, T. (2014). Investigating the impacts of epilepsy on EEG-based person identification systems. In D. Liu, & J. Si (Eds.), 2014 International Joint Conference on Neural Networks (IJCNN) (pp. 3644-3648). [6889567] (Proceedings of the International Joint Conference on Neural Networks). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2014.6889567