EEG-Based person verification using Multi-Sphere SVDD and UBM

Phuoc Nguyen, Dat Tran, Trung Le, Xu Huang, Wanli Ma

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

12 Citations (Scopus)

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 publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part I
EditorsJian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu
PublisherSpringer
Pages289-300
Number of pages12
ISBN (Print)9783642374524
DOIs
Publication statusPublished - 2013
Externally publishedYes

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume7818
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • EEG
  • Person verification
  • Support vector data description
  • Support vector machine
  • Universal background model

Cite this

Nguyen, P., Tran, D., Le, T., Huang, X., & Ma, W. (2013). EEG-Based person verification using Multi-Sphere SVDD and UBM. In J. Pei, V. S. Tseng, L. Cao, H. Motoda, & G. Xu (Eds.), Advances in Knowledge Discovery and Data Mining : 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part I (pp. 289-300). (Lecture Notes in Computer Science ; Vol. 7818 ). Springer. https://doi.org/10.1007/978-3-642-37453-1_24