EEG-based biometrics: effects of template ageing

Pablo Arnau-González*, Stamos Katsigiannis, Miguel Arevalillo-Herráez, Naeem Ramzan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

This chapter discusses the effects of template ageing in EEG-based biometrics. The chapter also serves as an introduction to general biometrics and its main tasks: Identification and verification. To do so, we investigate different characterisations of EEG signals and examine the difference of performance in subject identification between single session and cross-session identification experiments. In order to do this, EEG signals are characterised with common state-of-the-art features, i.e. Mel Frequency Cepstral Coefficients (MFCC), Autoregression Coefficients, and Power Spectral Density-derived features. The samples were later classified using various classifiers, including Support Vector Machines and k-Nearest Neighbours with different parametrisations. Results show that performance tends to be worse for cross-session identification compared to single session identification. This finding suggests that temporal permanence of EEG signals is limited and thus more sophisticated methods are needed in order to characterise EEG signals for the task of subject identification.
Original languageEnglish
Title of host publicationAI for Emerging Verticals
Subtitle of host publicationHuman-Robot Computing, Sensing and Networking
EditorsMuhammad Zeeshan Shakir, Naeem Ramzan
PublisherIET
Chapter11
ISBN (Electronic)9781785619830
ISBN (Print)9781785619823
Publication statusAccepted/In press - 9 Feb 2020

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