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Khatibi, Toktam, Zarean, Javad. (1403). Proposing a novel attention-based deep neural network (ABCL-EHI) for EEG-based human biometric identification. , 56(1), 123-145. doi: 10.22059/jac.2024.370579.1206
Toktam Khatibi; Javad Zarean. "Proposing a novel attention-based deep neural network (ABCL-EHI) for EEG-based human biometric identification". , 56, 1, 1403, 123-145. doi: 10.22059/jac.2024.370579.1206
Khatibi, Toktam, Zarean, Javad. (1403). 'Proposing a novel attention-based deep neural network (ABCL-EHI) for EEG-based human biometric identification', , 56(1), pp. 123-145. doi: 10.22059/jac.2024.370579.1206
Khatibi, Toktam, Zarean, Javad. Proposing a novel attention-based deep neural network (ABCL-EHI) for EEG-based human biometric identification. , 1403; 56(1): 123-145. doi: 10.22059/jac.2024.370579.1206

Proposing a novel attention-based deep neural network (ABCL-EHI) for EEG-based human biometric identification

Journal of Algorithms and Computation
مقاله 9، دوره 56، شماره 1، آبان 2024، صفحه 123-145    اصل مقاله (2.12 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jac.2024.370579.1206
نویسندگان
Toktam Khatibi* ؛ Javad Zarean
School of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
چکیده
The paper introduces a new method called ABCL-EHI for human identification using electroencephalographic (EEG) signals. EEG signals have unique information among individuals, but current systems lack accuracy and usability. ABCL-EHI addresses this by combining a convolutional neural network (CNN) and a long short-term memory (LSTM) network with an attention mechanism. This attention mechanism enhances the utilization of spatial and temporal characteristics of EEG signals. The proposed system is evaluated using a public dataset of EEG signals from 109 subjects performing motor/imagery tasks. The results demonstrate that ABCL-EHI achieves high accuracy, with F1-Score scores of 99.65, 99.65, and 99.52 when using 64, 14, and 9 EEG channels, respectively. This outperforms previous studies and highlights the system's reliability and ease of deployment in real-life applications, as it maintains high accuracy even with a small number of EEG channels and allows users to perform various tasks while recording signals.
کلیدواژه‌ها
Healthcare Data Analytics؛ Machine Learning؛ Physiological Signal Processing؛ CNN؛ LSTM
آمار
تعداد مشاهده مقاله: 894
تعداد دریافت فایل اصل مقاله: 756





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