ES1D: A Deep Network for EEG-Based Subject Identification

Arnau-González, Pablo; Katsigiannis, Stamos; Ramzan, Naeem; Tolson, Debbie; Arevalillo-Herráez, Miguel

Abstract

Security systems are starting to meet new technologies and new machine learning techniques, and a variety of methods to identify individuals from physiological signals have been developed. In this paper, we present ESID, a deep learning approach to identify subjects from electroencephalogram (EEG) signals captured by using a low cost device. The system consists of a Convolutional Neural Network (CNN), which is fed with the power spectral density of different EEG recordings belonging to different individuals. The network is trained for a period of one million iterations, in order to learn features related to local patterns in the spectral domain of the original signal. The performance of the system is evaluated against other traditional classification-based methods that use prior-knowledge-defined features. Results show that the system significantly outperforms other examined approaches, with 94% accuracy at discerning an individual in between a group of 23 different individuals.

Citation

Arnau-González, P., Katsigiannis, S., Ramzan, N., Tolson, D., & Arevalillo-Herráez, M. (2017, October). ES1D: A Deep Network for EEG-Based Subject Identification. Presented at IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE 2017), Washington, DC, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE 2017)
• Start Date: Oct 23, 2017
• End Date: Oct 25, 2017
• Online Publication Date: Jan 11, 2018
• Publication Date: Jul 1, 2017
• Deposit Date: Feb 25, 2025
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 81-85
• Series ISSN: 2471-7819
• Book Title: 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)
• DOI: https://doi.org/10.1109/BIBE.2017.00-74
• Public URL: https://durham-repository.worktribe.com/output/3546942