EEG-based person identification through binary flower pollination algorithm

Rodrigues, Douglas, Silva, Gabriel F. A., Papa, João P., Marana, Aparecido N. and Yang, Xin-She ORCID: (2016) EEG-based person identification through binary flower pollination algorithm. Expert Systems with Applications, 62 . pp. 81-90. ISSN 0957-4174 [Article] (doi:10.1016/j.eswa.2016.06.006)

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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.

Item Type: Article
Additional Information: Available online 14 June 2016
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 20903
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Depositing User: Xin-She Yang
Date Deposited: 04 Nov 2016 14:10
Last Modified: 12 Feb 2021 01:00

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