EEG-Based Person Verification Using Multi-Sphere SVDD and UBM

Phuoc NGUYEN, Dat TRAN, Trung Le, Xu HUANG, Wanli MA

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

6 Citations (Scopus)
1 Downloads (Pure)

Abstract

The use of brain-wave patterns extracted from electroencephalography (EEG) brain signals for person verification has been investigated recently. The challenge is that the EEG signals are noisy due to low conductivity of the human skull and the EEG data have unknown distribution. We propose a multi-sphere support vector data description (MSSVDD) method to reduce noise and to provide a mixture of hyperspheres that can describe the EEG data distribution. We also propose a MSSVDD universal background model (UBM) to model impostors in person verification. Experimental results show that our proposed methods achieved lower verification error rates than other verification methods.
Original languageEnglish
Title of host publicationPacific-Asia Conference on Knowledge Discovery and Data Mining
Subtitle of host publicationLecture Notes in Computer Science
EditorsRandy Goebel, Yuzuru Tanaka, Wolfgang Wahlster
Place of PublicationAustralia
PublisherSpringer
Pages289-300
Number of pages12
Volume7818
ISBN (Print)9783642374524
DOIs
Publication statusPublished - 2013
Event17th Pacific-Asia Conference on Knowledge Discovery and Data Mining - Gold Coast, Gold Coast, Australia
Duration: 14 Apr 201317 Apr 2013

Conference

Conference17th Pacific-Asia Conference on Knowledge Discovery and Data Mining
CountryAustralia
CityGold Coast
Period14/04/1317/04/13

Fingerprint

Electroencephalography
Data description
Brain

Cite this

NGUYEN, P., TRAN, D., Le, T., HUANG, X., & MA, W. (2013). EEG-Based Person Verification Using Multi-Sphere SVDD and UBM. In R. Goebel, Y. Tanaka, & W. Wahlster (Eds.), Pacific-Asia Conference on Knowledge Discovery and Data Mining: Lecture Notes in Computer Science (Vol. 7818, pp. 289-300). Australia: Springer. https://doi.org/10.1007/978-3-642-37453-1_24
NGUYEN, Phuoc ; TRAN, Dat ; Le, Trung ; HUANG, Xu ; MA, Wanli. / EEG-Based Person Verification Using Multi-Sphere SVDD and UBM. Pacific-Asia Conference on Knowledge Discovery and Data Mining: Lecture Notes in Computer Science. editor / Randy Goebel ; Yuzuru Tanaka ; Wolfgang Wahlster. Vol. 7818 Australia : Springer, 2013. pp. 289-300
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abstract = "The use of brain-wave patterns extracted from electroencephalography (EEG) brain signals for person verification has been investigated recently. The challenge is that the EEG signals are noisy due to low conductivity of the human skull and the EEG data have unknown distribution. We propose a multi-sphere support vector data description (MSSVDD) method to reduce noise and to provide a mixture of hyperspheres that can describe the EEG data distribution. We also propose a MSSVDD universal background model (UBM) to model impostors in person verification. Experimental results show that our proposed methods achieved lower verification error rates than other verification methods.",
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NGUYEN, P, TRAN, D, Le, T, HUANG, X & MA, W 2013, EEG-Based Person Verification Using Multi-Sphere SVDD and UBM. in R Goebel, Y Tanaka & W Wahlster (eds), Pacific-Asia Conference on Knowledge Discovery and Data Mining: Lecture Notes in Computer Science. vol. 7818, Springer, Australia, pp. 289-300, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Gold Coast, Australia, 14/04/13. https://doi.org/10.1007/978-3-642-37453-1_24

EEG-Based Person Verification Using Multi-Sphere SVDD and UBM. / NGUYEN, Phuoc; TRAN, Dat; Le, Trung; HUANG, Xu; MA, Wanli.

Pacific-Asia Conference on Knowledge Discovery and Data Mining: Lecture Notes in Computer Science. ed. / Randy Goebel; Yuzuru Tanaka; Wolfgang Wahlster. Vol. 7818 Australia : Springer, 2013. p. 289-300.

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

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N2 - The use of brain-wave patterns extracted from electroencephalography (EEG) brain signals for person verification has been investigated recently. The challenge is that the EEG signals are noisy due to low conductivity of the human skull and the EEG data have unknown distribution. We propose a multi-sphere support vector data description (MSSVDD) method to reduce noise and to provide a mixture of hyperspheres that can describe the EEG data distribution. We also propose a MSSVDD universal background model (UBM) to model impostors in person verification. Experimental results show that our proposed methods achieved lower verification error rates than other verification methods.

AB - The use of brain-wave patterns extracted from electroencephalography (EEG) brain signals for person verification has been investigated recently. The challenge is that the EEG signals are noisy due to low conductivity of the human skull and the EEG data have unknown distribution. We propose a multi-sphere support vector data description (MSSVDD) method to reduce noise and to provide a mixture of hyperspheres that can describe the EEG data distribution. We also propose a MSSVDD universal background model (UBM) to model impostors in person verification. Experimental results show that our proposed methods achieved lower verification error rates than other verification methods.

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NGUYEN P, TRAN D, Le T, HUANG X, MA W. EEG-Based Person Verification Using Multi-Sphere SVDD and UBM. In Goebel R, Tanaka Y, Wahlster W, editors, Pacific-Asia Conference on Knowledge Discovery and Data Mining: Lecture Notes in Computer Science. Vol. 7818. Australia: Springer. 2013. p. 289-300 https://doi.org/10.1007/978-3-642-37453-1_24