Age and Gender Classification Using EEG Paralinguistic Features

Dat TRAN, Xu HUANG, Wanli MA

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

    9 Citations (Scopus)

    Abstract

    The effects of age and gender on EEG signal have been investigated in clinical psychophysiology. However extracting age and gender information from EEG data has not been addressed. This information is useful in building automatic systems that can classify a person in to gender or age groups based on EEG characteristics of that person, index EEG data for searching, identify or verify a person, and improve brain-computer interface systems. We propose in this paper a framework of automatic age and gender classification system using EEG data. We also propose a speech-based method to extract paralinguistic features in EEG signal that contain rich age and gender information and apply these features to improve performance of our age and gender classification system. Experimental results for system evaluation and comparison are also presented
    Original languageEnglish
    Title of host publicationThe 6th IEEE/EMBS Conference on Neural Engineering (NER)
    EditorsKenji Sunagawa, Christian Roux, Toshiyo Tamura, Nigel Lovell, Masaaki Makikawa
    Place of PublicationUSA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages1295-1298
    Number of pages4
    Volume1
    ISBN (Electronic)9781467319690
    DOIs
    Publication statusPublished - 2013
    Event6th IEEE/EMBS Conference on Neural Engineering (NER) - San Diego, San Diego, United States
    Duration: 6 Nov 20138 Nov 2013

    Conference

    Conference6th IEEE/EMBS Conference on Neural Engineering (NER)
    CountryUnited States
    CitySan Diego
    Period6/11/138/11/13

    Fingerprint

    Electroencephalography
    Psychophysiology
    Brain computer interface

    Cite this

    TRAN, D., HUANG, X., & MA, W. (2013). Age and Gender Classification Using EEG Paralinguistic Features. In K. Sunagawa, C. Roux, T. Tamura, N. Lovell, & M. Makikawa (Eds.), The 6th IEEE/EMBS Conference on Neural Engineering (NER) (Vol. 1, pp. 1295-1298). USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/NER.2013.6696178
    TRAN, Dat ; HUANG, Xu ; MA, Wanli. / Age and Gender Classification Using EEG Paralinguistic Features. The 6th IEEE/EMBS Conference on Neural Engineering (NER). editor / Kenji Sunagawa ; Christian Roux ; Toshiyo Tamura ; Nigel Lovell ; Masaaki Makikawa. Vol. 1 USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 1295-1298
    @inproceedings{ff3a9eb8c9a44bd7855b08a5e01712db,
    title = "Age and Gender Classification Using EEG Paralinguistic Features",
    abstract = "The effects of age and gender on EEG signal have been investigated in clinical psychophysiology. However extracting age and gender information from EEG data has not been addressed. This information is useful in building automatic systems that can classify a person in to gender or age groups based on EEG characteristics of that person, index EEG data for searching, identify or verify a person, and improve brain-computer interface systems. We propose in this paper a framework of automatic age and gender classification system using EEG data. We also propose a speech-based method to extract paralinguistic features in EEG signal that contain rich age and gender information and apply these features to improve performance of our age and gender classification system. Experimental results for system evaluation and comparison are also presented",
    keywords = "Brain-computer interface, age and gender classification, Paralinguistic Features",
    author = "Dat TRAN and Xu HUANG and Wanli MA",
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    TRAN, D, HUANG, X & MA, W 2013, Age and Gender Classification Using EEG Paralinguistic Features. in K Sunagawa, C Roux, T Tamura, N Lovell & M Makikawa (eds), The 6th IEEE/EMBS Conference on Neural Engineering (NER). vol. 1, IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 1295-1298, 6th IEEE/EMBS Conference on Neural Engineering (NER), San Diego, United States, 6/11/13. https://doi.org/10.1109/NER.2013.6696178

    Age and Gender Classification Using EEG Paralinguistic Features. / TRAN, Dat; HUANG, Xu; MA, Wanli.

    The 6th IEEE/EMBS Conference on Neural Engineering (NER). ed. / Kenji Sunagawa; Christian Roux; Toshiyo Tamura; Nigel Lovell; Masaaki Makikawa. Vol. 1 USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 1295-1298.

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

    TY - GEN

    T1 - Age and Gender Classification Using EEG Paralinguistic Features

    AU - TRAN, Dat

    AU - HUANG, Xu

    AU - MA, Wanli

    PY - 2013

    Y1 - 2013

    N2 - The effects of age and gender on EEG signal have been investigated in clinical psychophysiology. However extracting age and gender information from EEG data has not been addressed. This information is useful in building automatic systems that can classify a person in to gender or age groups based on EEG characteristics of that person, index EEG data for searching, identify or verify a person, and improve brain-computer interface systems. We propose in this paper a framework of automatic age and gender classification system using EEG data. We also propose a speech-based method to extract paralinguistic features in EEG signal that contain rich age and gender information and apply these features to improve performance of our age and gender classification system. Experimental results for system evaluation and comparison are also presented

    AB - The effects of age and gender on EEG signal have been investigated in clinical psychophysiology. However extracting age and gender information from EEG data has not been addressed. This information is useful in building automatic systems that can classify a person in to gender or age groups based on EEG characteristics of that person, index EEG data for searching, identify or verify a person, and improve brain-computer interface systems. We propose in this paper a framework of automatic age and gender classification system using EEG data. We also propose a speech-based method to extract paralinguistic features in EEG signal that contain rich age and gender information and apply these features to improve performance of our age and gender classification system. Experimental results for system evaluation and comparison are also presented

    KW - Brain-computer interface

    KW - age and gender classification

    KW - Paralinguistic Features

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    BT - The 6th IEEE/EMBS Conference on Neural Engineering (NER)

    A2 - Sunagawa, Kenji

    A2 - Roux, Christian

    A2 - Tamura, Toshiyo

    A2 - Lovell, Nigel

    A2 - Makikawa, Masaaki

    PB - IEEE, Institute of Electrical and Electronics Engineers

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    TRAN D, HUANG X, MA W. Age and Gender Classification Using EEG Paralinguistic Features. In Sunagawa K, Roux C, Tamura T, Lovell N, Makikawa M, editors, The 6th IEEE/EMBS Conference on Neural Engineering (NER). Vol. 1. USA: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 1295-1298 https://doi.org/10.1109/NER.2013.6696178