Robust electroencephalogram-based biometric identification against GAN-generated artificial signals using a novel end-to-end attention-based CNN-LSTM neural network

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dc.authorscopusidReza Tavakkoli-Moghaddam / 57207533714
dc.authorwosidReza Tavakkoli-Moghaddam / P-1948-2015
dc.contributor.authorZarean, Javad
dc.contributor.authorTajally, AmirReza
dc.contributor.authorTavakkoli-Moghaddam, Reza
dc.contributor.authorKia, Reza
dc.date.accessioned2025-04-17T14:32:51Z
dc.date.available2025-04-17T14:32:51Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractElectroencephalogram (EEG) signals, which exhibit dynamic properties and discriminative information among individuals, have recently been employed to develop human biometric identification and authentication systems. Despite the increasing interest in EEG-based human identification, the state-of-the-art still needs high-accuracy and easy-to-use systems in real-life applications. To improve the accuracy, robustness, and user-friendliness of EEG-based human identification systems, this paper presents a novel attention-based convolutional-long short-term memory network for EEG-based human biometric identification (ABCL-EHBI), which is robust against artificial EEG signals generated by generative adversarial networks (GANs). The proposed system uses an attention mechanism along with convolutional neural networks (CNNs) and long short-term memories (LSTMs) layers, leading to more effective exploitation of the raw EEG signals' spatial and temporal discriminative characteristics compared to a simple CNN-LSTM (CL) system. The system was evaluated and validated using the PhysioNet motor imagery dataset, which incorporates EEG signals of 109 individuals performing six various tasks. Experimental results show that the proposed approach achieves F1-Score accuracy of 99.65, 99.64, and 99.55 under the condition of using 64, 14, and only 9 EEG channels, respectively, which is better than the performance of EEG-based human identification in the previous studies. The fact that the proposed approach receives raw EEG signals as input without any need for feature extraction (end-to-end), shows high accuracy when using a small number of EEG channels and yields high accuracy against artificial EEG signals, making it reliable and easy to deploy in real-life applications.
dc.identifier.citationZarean, J., Tajally, A., Tavakkoli-Moghaddam, R., & Kia, R. (2025). Robust electroencephalogram-based biometric identification against GAN-generated artificial signals using a novel end-to-end attention-based CNN-LSTM neural network. Cluster Computing, 28(3), 168.
dc.identifier.doi10.1007/s10586-024-04921-6
dc.identifier.endpage20
dc.identifier.issn1386-7857
dc.identifier.issn1573-7543
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85217283052
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1007/s10586-024-04921-6
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6301
dc.identifier.volume28
dc.identifier.wosWOS:001401573600008
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorTavakkoli-Moghaddam, Reza
dc.institutionauthoridReza Tavakkoli-Moghaddam / 0000-0002-6757-926X
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofCluster computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAttention Mechanism
dc.subjectDeep Learning
dc.subjectElectroencephalogram
dc.subjectGenerative Adversarial Networks
dc.subjectUser Identification
dc.titleRobust electroencephalogram-based biometric identification against GAN-generated artificial signals using a novel end-to-end attention-based CNN-LSTM neural network
dc.typeArticle

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