A Proposed Feature Extraction Method for EEG-based Person Identification

Dat Tran; Xu Huang; Dharmendra Sharma

A Proposed Feature Extraction Method for EEG-based Person Identification

Dat Tran, Xu Huang, Dharmendra Sharma

Information Systems

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

60 Citations (Scopus)

Abstract

We propose in this paper a feature extraction method to extract brain wave features from electroencephalography (EEG) signal. The proposed feature extraction method is based on an assumption that EEG signal could be considered as stationary if the time window is sufficiently short. With this assumption, EEG signal has some similar properties to speech signal and hence a feature extraction method that is currently used to extract speech features can be applied to extract brain wave features from EEG signal. Mel-frequency cepstral coefficients are features extracted and evaluated in EEG-based person identication. Experimental results show that the proposed method could provide very high recognition rate.

Original language	English
Title of host publication	ICAI 2012 : Proceedings of the 2012 International Conference on Artificial Intelligence, WORLDCOMP'12 : Volume II
Editors	Hamid Reza Arabnia
Place of Publication	USA
Publisher	CSREA Press
Pages	826-831
Number of pages	6
Volume	2
ISBN (Print)	9781601322173
Publication status	Published - 2012
Event	2012 International Conference on Artificial Intelligence - Las Vegas, Las Vegas, United States Duration: 16 Jul 2012 → 19 Jul 2012

Conference

Conference	2012 International Conference on Artificial Intelligence
Abbreviated title	ICAI 2012
Country/Territory	United States
City	Las Vegas
Period	16/07/12 → 19/07/12

Access to Document

http://world-comp.org/p2012/ICA4683.pdf

Cite this

@inproceedings{ca21229e1bd045d3b118dc90ddcc556b,

title = "A Proposed Feature Extraction Method for EEG-based Person Identification",

abstract = "We propose in this paper a feature extraction method to extract brain wave features from electroencephalography (EEG) signal. The proposed feature extraction method is based on an assumption that EEG signal could be considered as stationary if the time window is sufficiently short. With this assumption, EEG signal has some similar properties to speech signal and hence a feature extraction method that is currently used to extract speech features can be applied to extract brain wave features from EEG signal. Mel-frequency cepstral coefficients are features extracted and evaluated in EEG-based person identication. Experimental results show that the proposed method could provide very high recognition rate.",

author = "Dat Tran and Xu Huang and Dharmendra Sharma",

year = "2012",

language = "English",

isbn = "9781601322173",

volume = "2",

pages = "826--831",

editor = "Arabnia, {Hamid Reza}",

booktitle = "ICAI 2012 : Proceedings of the 2012 International Conference on Artificial Intelligence, WORLDCOMP'12 : Volume II",

publisher = "CSREA Press",

note = "2012 International Conference on Artificial Intelligence, ICAI 2012 ; Conference date: 16-07-2012 Through 19-07-2012",

}

Tran, D , Huang, X & Sharma, D 2012, A Proposed Feature Extraction Method for EEG-based Person Identification. in HR Arabnia (ed.), ICAI 2012 : Proceedings of the 2012 International Conference on Artificial Intelligence, WORLDCOMP'12 : Volume II. vol. 2, CSREA Press, USA, pp. 826-831, 2012 International Conference on Artificial Intelligence, Las Vegas, Nevada, United States, 16/07/12. <http://world-comp.org/p2012/ICA4683.pdf>

A Proposed Feature Extraction Method for EEG-based Person Identification. / Tran, Dat ; Huang, Xu ; Sharma, Dharmendra.
ICAI 2012 : Proceedings of the 2012 International Conference on Artificial Intelligence, WORLDCOMP'12 : Volume II. ed. / Hamid Reza Arabnia. Vol. 2 USA: CSREA Press, 2012. p. 826-831.

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

TY - GEN

T1 - A Proposed Feature Extraction Method for EEG-based Person Identification

AU - Tran, Dat

AU - Huang, Xu

AU - Sharma, Dharmendra

PY - 2012

Y1 - 2012

N2 - We propose in this paper a feature extraction method to extract brain wave features from electroencephalography (EEG) signal. The proposed feature extraction method is based on an assumption that EEG signal could be considered as stationary if the time window is sufficiently short. With this assumption, EEG signal has some similar properties to speech signal and hence a feature extraction method that is currently used to extract speech features can be applied to extract brain wave features from EEG signal. Mel-frequency cepstral coefficients are features extracted and evaluated in EEG-based person identication. Experimental results show that the proposed method could provide very high recognition rate.

AB - We propose in this paper a feature extraction method to extract brain wave features from electroencephalography (EEG) signal. The proposed feature extraction method is based on an assumption that EEG signal could be considered as stationary if the time window is sufficiently short. With this assumption, EEG signal has some similar properties to speech signal and hence a feature extraction method that is currently used to extract speech features can be applied to extract brain wave features from EEG signal. Mel-frequency cepstral coefficients are features extracted and evaluated in EEG-based person identication. Experimental results show that the proposed method could provide very high recognition rate.

M3 - Conference contribution

SN - 9781601322173

VL - 2

SP - 826

EP - 831

BT - ICAI 2012 : Proceedings of the 2012 International Conference on Artificial Intelligence, WORLDCOMP'12 : Volume II

A2 - Arabnia, Hamid Reza

PB - CSREA Press

CY - USA

T2 - 2012 International Conference on Artificial Intelligence

Y2 - 16 July 2012 through 19 July 2012

ER -