In-ear EEG biometrics for feasible and readily collectable real-world person authentication
File(s)
Published version
Author(s)
Nakamura, T
Goverdovsky, V
mandic, D
Type
Journal Article
Abstract
The use of EEG as a biometrics modality has been investigated for about a decade, however its feasibility in real-world applications is not yet conclusively established, mainly due to the issues with collectability and reproducibility. To this end, we propose a readily deployable EEG biometrics system based on a ‘one-fits-all’ viscoelastic generic in-ear EEG sensor (collectability), which does not require skilled assistance or cumbersome preparation. Unlike most existing studies, we consider data recorded over multiple recording days and for multiple subjects (reproducibility) while, for rigour, the training and test segments are not taken from the same recording days. A robust approach is considered based on the resting state with eyes closed paradigm, the use of both parametric (autoregressive model) and non-parametric (spectral) features, and supported by simple and fast cosine distance, linear discriminant analysis and support vector machine classifiers. Both the verification and identification forensics scenarios are considered and the achieved results are on par with the studies based on impractical on-scalp recordings. Comprehensive analysis over a number of subjects, setups, and analysis features demonstrates the feasibility of the proposed ear-EEG biometrics, and its potential in resolving the critical collectability, robustness, and reproducibility issues associated with current EEG biometrics.
Date Issued
2017-10-13
Date Acceptance
2017-10-09
Citation
IEEE Transactions on Information Forensics and Security, 2017, 13 (3), pp.648-661
ISSN
1556-6013
Publisher
Institute of Electrical and Electronics Engineers
Start Page
648
End Page
661
Journal / Book Title
IEEE Transactions on Information Forensics and Security
Volume
13
Issue
3
Copyright Statement
This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
License URL
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Rosetrees Trust
Grant Number
N/A
N/A
Subjects
Science & Technology
Technology
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Engineering
Wearable sensors
electroencephalography
biometrics
RECOGNITION
STABILITY
PERMANENCE
PATTERN
SLEEP
08 Information And Computing Sciences
09 Engineering
Strategic, Defence & Security Studies
Publication Status
Published