People Identification with RMS-Based Spatial Pattern of EEG Signal

Salahiddin Altahat, Xu Huang, Dat Tran, Dharmendra Sharma

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

2 Citations (Scopus)

Abstract

Recently, there are increasing interests in proposing novel people identification methods. In this work we propose to use root mean square (rms) to create a spatial pattern of the Electroencephalogram (EEG), and use this pattern in people identification. The proposed method is straight forward and has low cost of computation comparing to recent published methods such as auto regression (AR), independent component analysis (ICA) or wavelet. More importantly, the proposed method gives very promising results.
Original languageEnglish
Title of host publicationInternational Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2012)
Subtitle of host publicationLecture Notes in Computer Science
EditorsYang Xiang, Ivan Stojmenovic, Bernady O Apduhan, Guojun Wang, Nakano Koji, Y Zomaya Albert
Place of PublicationBerlin Heidelberg
PublisherSpringer
Pages310-318
Number of pages9
Volume7440
ISBN (Electronic)9783642330650
ISBN (Print)9783642330643
DOIs
Publication statusPublished - 2012
Event12th International Conference on Algorithms and Architectures for Parallel Processing ICA3PP 2012: ICA3PP 2012 - Fukuoka, Fukuoka, Japan
Duration: 4 Sep 20127 Sep 2012
http://nsclab.org/ica3pp12/

Conference

Conference12th International Conference on Algorithms and Architectures for Parallel Processing ICA3PP 2012
Abbreviated titleICA3PP 2012
CountryJapan
CityFukuoka
Period4/09/127/09/12
OtherICA3PP 2012 is the 12th in this series of conferences started in 1995 that are devoted to algorithms and architectures for parallel processing. ICA3PP is now recognized as the main regular event of the world that is covering the many dimensions of parallel algorithms and architectures, encompassing fundamental theoretical approaches, practical experimental projects, and commercial components and systems. As applications of computing systems have permeated in every aspects of daily life, the power of computing system has become increasingly critical. This conference provides a forum for countries around the world to exchange ideas for improving the computation power of computing systems.
Following the traditions of the previous successful ICA3PP conferences held in Hangzhou, Brisbane, Singapore, Melbourne, Hong Kong, Beijing, Cyprus, Taipei, Busan, and Melbourne, ICA3PP 2012 will be held in Fukuoka, Japan. The objective of ICA3PP 2012 is to bring together researchers and practitioners from academia, industry and governments to advance the theories and technologies in parallel and distributed computing. ICA3PP 2012 will focus on two broad areas of parallel and distributed computing, i.e., architectures, algorithms and networks, and systems and applications. The conference of ICA3PP 2012 will be organized by Kyushu Sangyo University, Japan
Internet address

Fingerprint

Independent component analysis
Electroencephalography
Costs

Cite this

Altahat, S., Huang, X., Tran, D., & Sharma, D. (2012). People Identification with RMS-Based Spatial Pattern of EEG Signal. In Y. Xiang, I. Stojmenovic, B. O. Apduhan, G. Wang, N. Koji, & Y. Z. Albert (Eds.), International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2012): Lecture Notes in Computer Science (Vol. 7440, pp. 310-318). Berlin Heidelberg: Springer. https://doi.org/10.1007/978-3-642-33065-0_33
Altahat, Salahiddin ; Huang, Xu ; Tran, Dat ; Sharma, Dharmendra. / People Identification with RMS-Based Spatial Pattern of EEG Signal. International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2012): Lecture Notes in Computer Science. editor / Yang Xiang ; Ivan Stojmenovic ; Bernady O Apduhan ; Guojun Wang ; Nakano Koji ; Y Zomaya Albert. Vol. 7440 Berlin Heidelberg : Springer, 2012. pp. 310-318
@inproceedings{3382719492ce47009547e8c7bc91c850,
title = "People Identification with RMS-Based Spatial Pattern of EEG Signal",
abstract = "Recently, there are increasing interests in proposing novel people identification methods. In this work we propose to use root mean square (rms) to create a spatial pattern of the Electroencephalogram (EEG), and use this pattern in people identification. The proposed method is straight forward and has low cost of computation comparing to recent published methods such as auto regression (AR), independent component analysis (ICA) or wavelet. More importantly, the proposed method gives very promising results.",
keywords = "EEG, Person identification, RMS brain signature, Brain biometrics",
author = "Salahiddin Altahat and Xu Huang and Dat Tran and Dharmendra Sharma",
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editor = "Yang Xiang and Ivan Stojmenovic and Apduhan, {Bernady O} and Guojun Wang and Nakano Koji and Albert, {Y Zomaya}",
booktitle = "International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2012)",
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Altahat, S, Huang, X, Tran, D & Sharma, D 2012, People Identification with RMS-Based Spatial Pattern of EEG Signal. in Y Xiang, I Stojmenovic, BO Apduhan, G Wang, N Koji & YZ Albert (eds), International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2012): Lecture Notes in Computer Science. vol. 7440, Springer, Berlin Heidelberg, pp. 310-318, 12th International Conference on Algorithms and Architectures for Parallel Processing ICA3PP 2012, Fukuoka, Japan, 4/09/12. https://doi.org/10.1007/978-3-642-33065-0_33

People Identification with RMS-Based Spatial Pattern of EEG Signal. / Altahat, Salahiddin; Huang, Xu; Tran, Dat; Sharma, Dharmendra.

International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2012): Lecture Notes in Computer Science. ed. / Yang Xiang; Ivan Stojmenovic; Bernady O Apduhan; Guojun Wang; Nakano Koji; Y Zomaya Albert. Vol. 7440 Berlin Heidelberg : Springer, 2012. p. 310-318.

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

TY - GEN

T1 - People Identification with RMS-Based Spatial Pattern of EEG Signal

AU - Altahat, Salahiddin

AU - Huang, Xu

AU - Tran, Dat

AU - Sharma, Dharmendra

PY - 2012

Y1 - 2012

N2 - Recently, there are increasing interests in proposing novel people identification methods. In this work we propose to use root mean square (rms) to create a spatial pattern of the Electroencephalogram (EEG), and use this pattern in people identification. The proposed method is straight forward and has low cost of computation comparing to recent published methods such as auto regression (AR), independent component analysis (ICA) or wavelet. More importantly, the proposed method gives very promising results.

AB - Recently, there are increasing interests in proposing novel people identification methods. In this work we propose to use root mean square (rms) to create a spatial pattern of the Electroencephalogram (EEG), and use this pattern in people identification. The proposed method is straight forward and has low cost of computation comparing to recent published methods such as auto regression (AR), independent component analysis (ICA) or wavelet. More importantly, the proposed method gives very promising results.

KW - EEG

KW - Person identification

KW - RMS brain signature

KW - Brain biometrics

U2 - 10.1007/978-3-642-33065-0_33

DO - 10.1007/978-3-642-33065-0_33

M3 - Conference contribution

SN - 9783642330643

VL - 7440

SP - 310

EP - 318

BT - International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2012)

A2 - Xiang, Yang

A2 - Stojmenovic, Ivan

A2 - Apduhan, Bernady O

A2 - Wang, Guojun

A2 - Koji, Nakano

A2 - Albert, Y Zomaya

PB - Springer

CY - Berlin Heidelberg

ER -

Altahat S, Huang X, Tran D, Sharma D. People Identification with RMS-Based Spatial Pattern of EEG Signal. In Xiang Y, Stojmenovic I, Apduhan BO, Wang G, Koji N, Albert YZ, editors, International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2012): Lecture Notes in Computer Science. Vol. 7440. Berlin Heidelberg: Springer. 2012. p. 310-318 https://doi.org/10.1007/978-3-642-33065-0_33