High-accuracy user identification using EEG biometrics

Toshiaki Koike-Akino, Ruhi Mahajan, Tim K. Marks, Ye Wang, Shinji Watanabe, Oncel Tuzel, Philip Orlik

研究成果: Conference contribution

26 被引用数 (Scopus)

抄録

We analyze brain waves acquired through a consumer-grade EEG device to investigate its capabilities for user identification and authentication. First, we show the statistical significance of the P300 component in event-related potential (ERP) data from 14-channel EEGs across 25 subjects. We then apply a variety of machine learning techniques, comparing the user identification performance of various different combinations of a dimensionality reduction technique followed by a classification algorithm. Experimental results show that an identification accuracy of 72% can be achieved using only a single 800 ms ERP epoch. In addition, we demonstrate that the user identification accuracy can be significantly improved to more than 96.7% by joint classification of multiple epochs.

本文言語English
ホスト出版物のタイトル2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
出版社Institute of Electrical and Electronics Engineers Inc.
ページ854-858
ページ数5
2016-October
ISBN(電子版)9781457702204
DOI
出版ステータスPublished - 2016 10 13
外部発表はい
イベント38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States
継続期間: 2016 8 162016 8 20

Other

Other38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
CountryUnited States
CityOrlando
Period16/8/1616/8/20

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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