Human Identification with electroencephalogram (EEG) for future network security

Xu HUANG, Salahiddin Altahat, Dat TRAN, Li Shutao

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

    1 Citation (Scopus)
    1 Downloads (Pure)

    Abstract

    Human identification becomes huge demand in particular for the security related areas, in particular for the network security. EEG signals are confidential and hard to imitate, since EEG signals are a reflection of individual-dependent inner mental tasks. Generally speaking, it has several advantages, such as (i) it is confidential as it corresponds to a mental task, (ii) it is very difficult to mimic and (iii) it is almost impossible to steal as the brain activity is sensitive to the stress and the mood of the person, an aggressor cannot force the person to reproduce his/her mental pass-phrase. In this paper we first proposed a novel algorithm to create a spatial pattern of EEG signals obtained from the open public database. In our EEG signal processing, we have analyzed 64-electrode EEG samples for two databases, one is for 45 people and calculate the equivalent root mean square (rms) values for each electrode signal over 1 second period, by which created a 64-value input for each subject. With this neural network (NN) model, our analysis clearly showed that our designed classifier is able to identify all the 45 people correctly (successful rate of 100%) with a mean square error of 2.0334(10− 7 and the same algorithm applying to the 2nd database with 116 out of 122 people can be fully identified (successful rate of 95.1%) with a mean square error value of 0.00186. We deeply believe that a low complexity, high resolution, effective and efficient is very attractive for the real life applications especially for network security in the foreseeable future.
    Original languageEnglish
    Title of host publicationInternational Conference on Network and System Security (NSS 2013)
    Subtitle of host publicationLecture Notes in Computer Science
    EditorsJavier Lopez, Xinyi Huang, Ravi Sandhu
    Place of PublicationSpain
    PublisherSpringer
    Pages575-581
    Number of pages7
    Volume7873
    ISBN (Print)9783642386305
    DOIs
    Publication statusPublished - 2013
    Event7th International Conference on Network and System Security, NSS 2013 - Madrid, Madrid, Spain
    Duration: 3 Jun 20134 Jun 2013

    Conference

    Conference7th International Conference on Network and System Security, NSS 2013
    CountrySpain
    CityMadrid
    Period3/06/134/06/13

    Fingerprint

    Network security
    Electroencephalography
    Mean square error
    Electrodes
    Brain
    Signal processing
    Classifiers
    Neural networks

    Cite this

    HUANG, X., Altahat, S., TRAN, D., & Shutao, L. (2013). Human Identification with electroencephalogram (EEG) for future network security. In J. Lopez, X. Huang, & R. Sandhu (Eds.), International Conference on Network and System Security (NSS 2013): Lecture Notes in Computer Science (Vol. 7873, pp. 575-581). Spain: Springer. https://doi.org/10.1007/978-3-642-38631-2_42
    HUANG, Xu ; Altahat, Salahiddin ; TRAN, Dat ; Shutao, Li. / Human Identification with electroencephalogram (EEG) for future network security. International Conference on Network and System Security (NSS 2013): Lecture Notes in Computer Science. editor / Javier Lopez ; Xinyi Huang ; Ravi Sandhu. Vol. 7873 Spain : Springer, 2013. pp. 575-581
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    abstract = "Human identification becomes huge demand in particular for the security related areas, in particular for the network security. EEG signals are confidential and hard to imitate, since EEG signals are a reflection of individual-dependent inner mental tasks. Generally speaking, it has several advantages, such as (i) it is confidential as it corresponds to a mental task, (ii) it is very difficult to mimic and (iii) it is almost impossible to steal as the brain activity is sensitive to the stress and the mood of the person, an aggressor cannot force the person to reproduce his/her mental pass-phrase. In this paper we first proposed a novel algorithm to create a spatial pattern of EEG signals obtained from the open public database. In our EEG signal processing, we have analyzed 64-electrode EEG samples for two databases, one is for 45 people and calculate the equivalent root mean square (rms) values for each electrode signal over 1 second period, by which created a 64-value input for each subject. With this neural network (NN) model, our analysis clearly showed that our designed classifier is able to identify all the 45 people correctly (successful rate of 100{\%}) with a mean square error of 2.0334(10− 7 and the same algorithm applying to the 2nd database with 116 out of 122 people can be fully identified (successful rate of 95.1{\%}) with a mean square error value of 0.00186. We deeply believe that a low complexity, high resolution, effective and efficient is very attractive for the real life applications especially for network security in the foreseeable future.",
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    HUANG, X, Altahat, S, TRAN, D & Shutao, L 2013, Human Identification with electroencephalogram (EEG) for future network security. in J Lopez, X Huang & R Sandhu (eds), International Conference on Network and System Security (NSS 2013): Lecture Notes in Computer Science. vol. 7873, Springer, Spain, pp. 575-581, 7th International Conference on Network and System Security, NSS 2013, Madrid, Spain, 3/06/13. https://doi.org/10.1007/978-3-642-38631-2_42

    Human Identification with electroencephalogram (EEG) for future network security. / HUANG, Xu; Altahat, Salahiddin; TRAN, Dat; Shutao, Li.

    International Conference on Network and System Security (NSS 2013): Lecture Notes in Computer Science. ed. / Javier Lopez; Xinyi Huang; Ravi Sandhu. Vol. 7873 Spain : Springer, 2013. p. 575-581.

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

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    AU - Altahat, Salahiddin

    AU - TRAN, Dat

    AU - Shutao, Li

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    N2 - Human identification becomes huge demand in particular for the security related areas, in particular for the network security. EEG signals are confidential and hard to imitate, since EEG signals are a reflection of individual-dependent inner mental tasks. Generally speaking, it has several advantages, such as (i) it is confidential as it corresponds to a mental task, (ii) it is very difficult to mimic and (iii) it is almost impossible to steal as the brain activity is sensitive to the stress and the mood of the person, an aggressor cannot force the person to reproduce his/her mental pass-phrase. In this paper we first proposed a novel algorithm to create a spatial pattern of EEG signals obtained from the open public database. In our EEG signal processing, we have analyzed 64-electrode EEG samples for two databases, one is for 45 people and calculate the equivalent root mean square (rms) values for each electrode signal over 1 second period, by which created a 64-value input for each subject. With this neural network (NN) model, our analysis clearly showed that our designed classifier is able to identify all the 45 people correctly (successful rate of 100%) with a mean square error of 2.0334(10− 7 and the same algorithm applying to the 2nd database with 116 out of 122 people can be fully identified (successful rate of 95.1%) with a mean square error value of 0.00186. We deeply believe that a low complexity, high resolution, effective and efficient is very attractive for the real life applications especially for network security in the foreseeable future.

    AB - Human identification becomes huge demand in particular for the security related areas, in particular for the network security. EEG signals are confidential and hard to imitate, since EEG signals are a reflection of individual-dependent inner mental tasks. Generally speaking, it has several advantages, such as (i) it is confidential as it corresponds to a mental task, (ii) it is very difficult to mimic and (iii) it is almost impossible to steal as the brain activity is sensitive to the stress and the mood of the person, an aggressor cannot force the person to reproduce his/her mental pass-phrase. In this paper we first proposed a novel algorithm to create a spatial pattern of EEG signals obtained from the open public database. In our EEG signal processing, we have analyzed 64-electrode EEG samples for two databases, one is for 45 people and calculate the equivalent root mean square (rms) values for each electrode signal over 1 second period, by which created a 64-value input for each subject. With this neural network (NN) model, our analysis clearly showed that our designed classifier is able to identify all the 45 people correctly (successful rate of 100%) with a mean square error of 2.0334(10− 7 and the same algorithm applying to the 2nd database with 116 out of 122 people can be fully identified (successful rate of 95.1%) with a mean square error value of 0.00186. We deeply believe that a low complexity, high resolution, effective and efficient is very attractive for the real life applications especially for network security in the foreseeable future.

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    HUANG X, Altahat S, TRAN D, Shutao L. Human Identification with electroencephalogram (EEG) for future network security. In Lopez J, Huang X, Sandhu R, editors, International Conference on Network and System Security (NSS 2013): Lecture Notes in Computer Science. Vol. 7873. Spain: Springer. 2013. p. 575-581 https://doi.org/10.1007/978-3-642-38631-2_42