Electroencephalograph (EEG)-based vigilance estimation methods have achieved significant progress. In vigilance-associated EEG signals, non-adjacent electrodes exhibit strong coupling, and distant time points also demonstrate significant dependence, not limited the adjacent ones. Therefore, fully extracting such rich global-local spatiotemporal characteristics is critical. In this paper, we propose the Global Enhanced LSTM Network (GE-LSTM-Net), in which synergizes the Transformer’s attention mechanism with LSTM to enhance the extraction of spatiotemporal features. Firstly, a specialized sample partitioning strategy along with the designed feature fusion module is adopted to reorganize raw EEG signals into structured 3D differential entropy (DE) feature representations, effectively preserving spatiotemporal and frequency dependencies across electrode channels and time points. Secondly, the attention mechanism and LSTM are encapsulated into a novel module (GE-LSTM module), serving as the core of the proposed GE-LSTM-Net to simultaneously extract spatiotemporal features from 3D representations. In this module, the attention mechanism will extract global information and integrate it into each unit of the LSTM, enabling LSTM to focus on more critical electrode channels and time points and extract richer global-local features. Subsequently, the GE-LSTM-Net demonstrate competitive performance and achieved SOTA results compared to existing methods on two public vigilance datasets. The codes are available at: https://github.com/Lanhao23-nudt/GE-LSTM-Net.
Lan et al. (Fri,) studied this question.