Identifying people using RMS spatial pattern of EEG signal

Salahiddin Altahat, Xu Huang, Dat Tran, Dharmendra Sharma

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

    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, Koji Nakano, Albert Zomaya
    Place of PublicationJapan
    PublisherSpringer
    Pages310-318
    Number of pages9
    Volume7440
    ISBN (Electronic)9783642330650
    ISBN (Print)9783642330643
    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

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    Independent component analysis
    Electroencephalography
    Costs

    Cite this

    Altahat, S., Huang, X., Tran, D., & Sharma, D. (2012). Identifying people using RMS spatial pattern of EEG signal. In Y. Xiang, I. Stojmenovic, B. O. Apduhan, G. Wang, K. Nakano, & A. Zomaya (Eds.), International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2012): Lecture Notes in Computer Science (Vol. 7440, pp. 310-318). Japan: Springer.
    Altahat, Salahiddin ; Huang, Xu ; Tran, Dat ; Sharma, Dharmendra. / Identifying people using RMS 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 ; Koji Nakano ; Albert Zomaya. Vol. 7440 Japan : Springer, 2012. pp. 310-318
    @inproceedings{d91296420218434b94df345b71a3bb7d,
    title = "Identifying people using RMS 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 = "Person identification, EEG",
    author = "Salahiddin Altahat and Xu Huang and Dat Tran and Dharmendra Sharma",
    year = "2012",
    language = "English",
    isbn = "9783642330643",
    volume = "7440",
    pages = "310--318",
    editor = "Yang Xiang and Ivan Stojmenovic and Apduhan, {Bernady O} and Guojun Wang and Koji Nakano and Albert Zomaya",
    booktitle = "International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2012)",
    publisher = "Springer",
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    }

    Altahat, S, Huang, X, Tran, D & Sharma, D 2012, Identifying people using RMS spatial pattern of EEG signal. in Y Xiang, I Stojmenovic, BO Apduhan, G Wang, K Nakano & A Zomaya (eds), International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2012): Lecture Notes in Computer Science. vol. 7440, Springer, Japan, pp. 310-318, 12th International Conference on Algorithms and Architectures for Parallel Processing ICA3PP 2012, Fukuoka, Japan, 4/09/12.

    Identifying people using RMS 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; Koji Nakano; Albert Zomaya. Vol. 7440 Japan : Springer, 2012. p. 310-318.

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

    TY - GEN

    T1 - Identifying people using RMS 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 - Person identification

    KW - EEG

    UR - https://link.springer.com/chapter/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 - Nakano, Koji

    A2 - Zomaya, Albert

    PB - Springer

    CY - Japan

    ER -

    Altahat S, Huang X, Tran D, Sharma D. Identifying people using RMS spatial pattern of EEG signal. In Xiang Y, Stojmenovic I, Apduhan BO, Wang G, Nakano K, Zomaya A, editors, International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2012): Lecture Notes in Computer Science. Vol. 7440. Japan: Springer. 2012. p. 310-318