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

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.