Using Shannon Entropy as EEG Signal Feature for Fast Person Identification

Dat TRAN, Wanli MA, Phuoc Nguyen

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

26 Citations (Scopus)

Abstract

Identification accuracy and speed are important factors in automatic person identification systems. In this paper, we propose a feature extraction method to extract brain wave features from different brain rhythms of electroencephalography (EEG) signal for the purpose of fast, yet accurate person identification. The proposed feature extraction method is based on the fact that EEG signal is complex, non-stationary, and non-linear. With this fact, non-linear analysis like entropy would be more appropriate. Shannon entropy (SE) based EEG features from alpha, beta, and gamma wave bands are extracted and evaluated for person identification. Experimental results show that SE features provide high person identification rates yet with a low feature dimension, thus better performance.
Original languageEnglish
Title of host publication22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings
EditorsMichel Verleysen
Place of PublicationBelgium
PublisherSymposium on Artifical Neural Networks
Pages413-418
Number of pages6
Volume1
ISBN (Electronic)9782874190957
ISBN (Print)9782874190957
Publication statusPublished - 23 Apr 2014
EventEuropean Symposium on Artificial Neural Networks 2014: ESANN 2014 - Brugge, Brugge, Belgium
Duration: 23 Apr 201425 Apr 2014

Publication series

Name22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings

Conference

ConferenceEuropean Symposium on Artificial Neural Networks 2014
Abbreviated titleESANN 2014
CountryBelgium
CityBrugge
Period23/04/1425/04/14

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  • Cite this

    TRAN, D., MA, W., & Nguyen, P. (2014). Using Shannon Entropy as EEG Signal Feature for Fast Person Identification. In M. Verleysen (Ed.), 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings (Vol. 1, pp. 413-418). (22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings). Symposium on Artifical Neural Networks. https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2014-121.pdf