EEG-Based User Authentication Using a Convolutional Neural Network

Ting Yu, Chun-Shu Wei, Kuan Jung Chiang, Masaki Nakanishi, Tzyy Ping Jung

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

1 Scopus citations

Abstract

In this study, we explore the feasibility of using a convolutional neural network (CNN) to decode human electroencephalographic (EEG) response for user authentication. In particular, we exploit the low-frequency components of the steady-state visual-evoked potentials (SSVEP) that contain consistent individualized patterns as the biometric. We evaluate the discriminating capabilities across different parameter configurations to optimize the CNN model. We also investigate how the length of EEG data impact the authentication performance. Our proposed framework achieved ~97% accuracy of crossday user authentication across 8 subjects, shedding light on a practical EEG-based biometric powered by the CNN-based brain decoding.

Original languageEnglish
Title of host publication9th International IEEE EMBS Conference on Neural Engineering, NER 2019
PublisherIEEE Computer Society
Pages1011-1014
Number of pages4
ISBN (Electronic)9781538679210
DOIs
StatePublished - 16 May 2019
Event9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States
Duration: 20 Mar 201923 Mar 2019

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2019-March
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference9th International IEEE EMBS Conference on Neural Engineering, NER 2019
CountryUnited States
CitySan Francisco
Period20/03/1923/03/19

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