Using Shannon Entropy as EEG Signal Feature for Fast Person Identification

Dat TRAN; Wanli MA; Phuoc Nguyen

Using Shannon Entropy as EEG Signal Feature for Fast Person Identification

Dat TRAN, Wanli MA, Phuoc Nguyen

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

62 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 language	English
Title of host publication	22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings
Editors	Michel Verleysen
Place of Publication	Belgium
Publisher	Symposium on Artifical Neural Networks
Pages	413-418
Number of pages	6
Volume	1
ISBN (Electronic)	9782874190957
ISBN (Print)	9782874190957
Publication status	Published - 23 Apr 2014
Event	European Symposium on Artificial Neural Networks 2014: ESANN 2014 - Brugge, Brugge, Belgium Duration: 23 Apr 2014 → 25 Apr 2014

Publication series

Name	22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings

Conference

Conference	European Symposium on Artificial Neural Networks 2014
Abbreviated title	ESANN 2014
Country/Territory	Belgium
City	Brugge
Period	23/04/14 → 25/04/14

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https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2014-121.pdf

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

TRAN, Dat ; MA, Wanli ; Nguyen, Phuoc. / Using Shannon Entropy as EEG Signal Feature for Fast Person Identification. 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings. editor / Michel Verleysen. Vol. 1 Belgium : Symposium on Artifical Neural Networks, 2014. pp. 413-418 (22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings).

@inproceedings{5be2b447efdb4c6b8f2ab15d7cc88bc8,

title = "Using Shannon Entropy as EEG Signal Feature for Fast Person Identification",

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.",

keywords = "Shannon-Entropy, Person-Identification, EEG",

author = "Dat TRAN and Wanli MA and Phuoc Nguyen",

year = "2014",

month = apr,

day = "23",

language = "English",

isbn = "9782874190957",

volume = "1",

series = "22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings",

publisher = "Symposium on Artifical Neural Networks",

pages = "413--418",

editor = "Michel Verleysen",

booktitle = "22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings",

note = "European Symposium on Artificial Neural Networks 2014 : ESANN 2014, ESANN 2014 ; Conference date: 23-04-2014 Through 25-04-2014",

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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, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings, Symposium on Artifical Neural Networks, Belgium, pp. 413-418, European Symposium on Artificial Neural Networks 2014, Brugge, Belgium, 23/04/14. <https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2014-121.pdf>

Using Shannon Entropy as EEG Signal Feature for Fast Person Identification. / TRAN, Dat; MA, Wanli; Nguyen, Phuoc.
22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings. ed. / Michel Verleysen. Vol. 1 Belgium: Symposium on Artifical Neural Networks, 2014. p. 413-418 (22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Using Shannon Entropy as EEG Signal Feature for Fast Person Identification

AU - TRAN, Dat

AU - MA, Wanli

AU - Nguyen, Phuoc

PY - 2014/4/23

Y1 - 2014/4/23

N2 - 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.

AB - 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.

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KW - Person-Identification

KW - EEG

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M3 - Conference contribution

SN - 9782874190957

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BT - 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings

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PB - Symposium on Artifical Neural Networks

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T2 - European Symposium on Artificial Neural Networks 2014

Y2 - 23 April 2014 through 25 April 2014

ER -

TRAN D, MA W, Nguyen P. Using Shannon Entropy as EEG Signal Feature for Fast Person Identification. In Verleysen M, editor, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings. Vol. 1. Belgium: Symposium on Artifical Neural Networks. 2014. p. 413-418. (22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings).

Using Shannon Entropy as EEG Signal Feature for Fast Person Identification

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