EEG-based person identification through binary flower pollination algorithm
Article
Rodrigues, D., Silva, G., Papa, J., Marana, A. and Yang, X. 2016. EEG-based person identification through binary flower pollination algorithm. Expert Systems with Applications. 62, pp. 81-90. https://doi.org/10.1016/j.eswa.2016.06.006
Type | Article |
---|---|
Title | EEG-based person identification through binary flower pollination algorithm |
Authors | Rodrigues, D., Silva, G., Papa, J., Marana, A. and Yang, X. |
Abstract | Electroencephalogram (EEG) signal presents a great potential for highly secure biometric systems due to its characteristics of universality, uniqueness, and natural robustness to spoofing attacks. EEG signals are measured by sensors placed in various positions of a person’s head (channels). In this work, we address the problem of reducing the number of required sensors while maintaining a comparable performance. We evaluated a binary version of the Flower Pollination Algorithm under different transfer functions to select the best subset of channels that maximizes the accuracy, which is measured by means of the Optimum-Path Forest classifier. The experimental results show the proposed approach can make use of less than a half of the number of sensors while maintaining recognition rates up to 87%, which is crucial towards the effective use of EEG in biometric applications. |
Keywords | Meta-heuristic; Pattern classification; Biometrics; Electroencephalogram; Optimum-path forest |
Publisher | Elsevier |
Journal | Expert Systems with Applications |
ISSN | 0957-4174 |
Electronic | 1873-6793 |
Publication dates | |
Online | 14 Jun 2016 |
15 Nov 2016 | |
Publication process dates | |
Deposited | 04 Nov 2016 |
Accepted | 04 Jun 2016 |
Output status | Published |
Accepted author manuscript | License File Access Level Open |
Copyright Statement | © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.eswa.2016.06.006 |
Web of Science identifier | WOS:000380626000006 |
Language | English |
https://repository.mdx.ac.uk/item/86v4w
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