Human Identification with Electroencephalogram (EEG) Signal Processing

Xu Huang, Salahiddin Altahat, Dat Tran, Dharmendra Sharma

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

15 Citations (Scopus)

Abstract

Human identification becomes huge demand in particular for the security related areas. Biometric systems can employ different kinds of features, e.g., features of fingerprint, face, iris or posture. EEG signals are the signature of neural activities. It is confidential and hard to imitate, since EEG signals are a reflection of individual-dependent inner mental tasks. 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 neural network (NN) model, our analysis 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 become true in the foreseeable future
Original languageEnglish
Title of host publication2012 International Symposium on Communications and Information Technologies
EditorsSalim Bouzerdoum, Michael Heinlich, Ren Oing Liu
Place of PublicationAustralia
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1026-1031
Number of pages6
Volume1
ISBN (Print)9781467311557
DOIs
Publication statusPublished - 2012
EventInternational Symposium on Communications and Information Technologies (ISCIT 2012) - Gold Coast, Gold Coast, Australia
Duration: 2 Oct 20125 Oct 2012

Conference

ConferenceInternational Symposium on Communications and Information Technologies (ISCIT 2012)
CountryAustralia
CityGold Coast
Period2/10/125/10/12

Fingerprint

Electroencephalography
Signal processing
Mean square error
Electrodes
Biometrics
Brain
Classifiers
Neural networks

Cite this

Huang, X., Altahat, S., Tran, D., & Sharma, D. (2012). Human Identification with Electroencephalogram (EEG) Signal Processing. In S. Bouzerdoum, M. Heinlich, & R. O. Liu (Eds.), 2012 International Symposium on Communications and Information Technologies (Vol. 1, pp. 1026-1031). Australia: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISCIT.2012.6380841
Huang, Xu ; Altahat, Salahiddin ; Tran, Dat ; Sharma, Dharmendra. / Human Identification with Electroencephalogram (EEG) Signal Processing. 2012 International Symposium on Communications and Information Technologies. editor / Salim Bouzerdoum ; Michael Heinlich ; Ren Oing Liu. Vol. 1 Australia : IEEE, Institute of Electrical and Electronics Engineers, 2012. pp. 1026-1031
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abstract = "Human identification becomes huge demand in particular for the security related areas. Biometric systems can employ different kinds of features, e.g., features of fingerprint, face, iris or posture. EEG signals are the signature of neural activities. It is confidential and hard to imitate, since EEG signals are a reflection of individual-dependent inner mental tasks. 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 neural network (NN) model, our analysis 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 become true in the foreseeable future",
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Huang, X, Altahat, S, Tran, D & Sharma, D 2012, Human Identification with Electroencephalogram (EEG) Signal Processing. in S Bouzerdoum, M Heinlich & RO Liu (eds), 2012 International Symposium on Communications and Information Technologies. vol. 1, IEEE, Institute of Electrical and Electronics Engineers, Australia, pp. 1026-1031, International Symposium on Communications and Information Technologies (ISCIT 2012), Gold Coast, Australia, 2/10/12. https://doi.org/10.1109/ISCIT.2012.6380841

Human Identification with Electroencephalogram (EEG) Signal Processing. / Huang, Xu; Altahat, Salahiddin; Tran, Dat; Sharma, Dharmendra.

2012 International Symposium on Communications and Information Technologies. ed. / Salim Bouzerdoum; Michael Heinlich; Ren Oing Liu. Vol. 1 Australia : IEEE, Institute of Electrical and Electronics Engineers, 2012. p. 1026-1031.

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

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AU - Sharma, Dharmendra

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N2 - Human identification becomes huge demand in particular for the security related areas. Biometric systems can employ different kinds of features, e.g., features of fingerprint, face, iris or posture. EEG signals are the signature of neural activities. It is confidential and hard to imitate, since EEG signals are a reflection of individual-dependent inner mental tasks. 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 neural network (NN) model, our analysis 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 become true in the foreseeable future

AB - Human identification becomes huge demand in particular for the security related areas. Biometric systems can employ different kinds of features, e.g., features of fingerprint, face, iris or posture. EEG signals are the signature of neural activities. It is confidential and hard to imitate, since EEG signals are a reflection of individual-dependent inner mental tasks. 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 neural network (NN) model, our analysis 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 become true in the foreseeable future

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M3 - Conference contribution

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BT - 2012 International Symposium on Communications and Information Technologies

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Huang X, Altahat S, Tran D, Sharma D. Human Identification with Electroencephalogram (EEG) Signal Processing. In Bouzerdoum S, Heinlich M, Liu RO, editors, 2012 International Symposium on Communications and Information Technologies. Vol. 1. Australia: IEEE, Institute of Electrical and Electronics Engineers. 2012. p. 1026-1031 https://doi.org/10.1109/ISCIT.2012.6380841