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)
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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.
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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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