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    Robust electroencephalogram-based biometric identification against GAN-generated artificial signals using a novel end-to-end attention-based CNN-LSTM neural network
    (Springer, 2025) Zarean, Javad; Tajally, AmirReza; Tavakkoli-Moghaddam, Reza; Kia, Reza
    Electroencephalogram (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.