Motor imagery EEG-based person verification

Dat Tran, Xu Huang, Wanli Ma

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

11 Citations (Scopus)
3 Downloads (Pure)

Abstract

We investigate in this paper the activity-dependent person verification method using electroencephalography (EEG) signal from a person performing motor imagery tasks. Two tasks were performed in our experiments were performed. In the first task, the same motor imagery task of left hand or right hand was applied to all persons. In the second task, only the best motor imagery task for each person was performed. The Gaussian mixture model (GMM) and support vector data description (SVDD) methods were used for modelling persons. Experimental results showed that lowest person verification error rate could be achieved when each person performed his/her best motor imagery task
Original languageEnglish
Title of host publicationInternational Work-Conference on Artificial Neural Networks
Subtitle of host publicationAdvances in Computational Intelligence IWANN 2013
EditorsIgnacio Rojas, Gonzalo Joya, Joan Gabestany
Place of PublicationBerlin
PublisherSpringer
Pages430-438
Number of pages9
Volume2
ISBN (Electronic)9783642386824
ISBN (Print)9783642386817
DOIs
Publication statusPublished - 2013
Event12th International Work-Conference on Artificial Neural Networks, IWANN 2013 - Tenerife, Tenerife, Spain
Duration: 12 Jun 201314 Jun 2013

Conference

Conference12th International Work-Conference on Artificial Neural Networks, IWANN 2013
Abbreviated titleIWANN 2013
CountrySpain
CityTenerife
Period12/06/1314/06/13

Fingerprint

Electroencephalography
Data description
Experiments

Cite this

Tran, D., Huang, X., & Ma, W. (2013). Motor imagery EEG-based person verification. In I. Rojas, G. Joya, & J. Gabestany (Eds.), International Work-Conference on Artificial Neural Networks: Advances in Computational Intelligence IWANN 2013 (Vol. 2, pp. 430-438). Berlin: Springer. https://doi.org/10.1007/978-3-642-38682-4_46
Tran, Dat ; Huang, Xu ; Ma, Wanli. / Motor imagery EEG-based person verification. International Work-Conference on Artificial Neural Networks: Advances in Computational Intelligence IWANN 2013. editor / Ignacio Rojas ; Gonzalo Joya ; Joan Gabestany. Vol. 2 Berlin : Springer, 2013. pp. 430-438
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title = "Motor imagery EEG-based person verification",
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Tran, D, Huang, X & Ma, W 2013, Motor imagery EEG-based person verification. in I Rojas, G Joya & J Gabestany (eds), International Work-Conference on Artificial Neural Networks: Advances in Computational Intelligence IWANN 2013. vol. 2, Springer, Berlin, pp. 430-438, 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Tenerife, Spain, 12/06/13. https://doi.org/10.1007/978-3-642-38682-4_46

Motor imagery EEG-based person verification. / Tran, Dat; Huang, Xu; Ma, Wanli.

International Work-Conference on Artificial Neural Networks: Advances in Computational Intelligence IWANN 2013. ed. / Ignacio Rojas; Gonzalo Joya; Joan Gabestany. Vol. 2 Berlin : Springer, 2013. p. 430-438.

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

TY - GEN

T1 - Motor imagery EEG-based person verification

AU - Tran, Dat

AU - Huang, Xu

AU - Ma, Wanli

PY - 2013

Y1 - 2013

N2 - We investigate in this paper the activity-dependent person verification method using electroencephalography (EEG) signal from a person performing motor imagery tasks. Two tasks were performed in our experiments were performed. In the first task, the same motor imagery task of left hand or right hand was applied to all persons. In the second task, only the best motor imagery task for each person was performed. The Gaussian mixture model (GMM) and support vector data description (SVDD) methods were used for modelling persons. Experimental results showed that lowest person verification error rate could be achieved when each person performed his/her best motor imagery task

AB - We investigate in this paper the activity-dependent person verification method using electroencephalography (EEG) signal from a person performing motor imagery tasks. Two tasks were performed in our experiments were performed. In the first task, the same motor imagery task of left hand or right hand was applied to all persons. In the second task, only the best motor imagery task for each person was performed. The Gaussian mixture model (GMM) and support vector data description (SVDD) methods were used for modelling persons. Experimental results showed that lowest person verification error rate could be achieved when each person performed his/her best motor imagery task

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KW - EEG

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KW - Person Verification

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SN - 9783642386817

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BT - International Work-Conference on Artificial Neural Networks

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Tran D, Huang X, Ma W. Motor imagery EEG-based person verification. In Rojas I, Joya G, Gabestany J, editors, International Work-Conference on Artificial Neural Networks: Advances in Computational Intelligence IWANN 2013. Vol. 2. Berlin: Springer. 2013. p. 430-438 https://doi.org/10.1007/978-3-642-38682-4_46