Combining Spatio-Temporal Networks and Graph Attention Architectures for EEG-Based Workload Classification

Estimation of cognitive workload from EEG signals is a key challenge in advancing neuroergonomic systems and brain-computer interfaces (BCIs). A hybrid approach is presented, combining EEGNet and Graph Attention Networks (GATs) to effectively capture the intricate spatial and temporal dynamics within EEG data. Leveraging the COG-BCI dataset, which includes cognitively demanding tasks such as N-Back and Multi-Attribute Task Battery-II (MATB-II), the model employs GAT’s multi-head attention mechanism to enhance feature extraction and improve classification performance. A dynamic graph, constructed by integrating spatial distances between electrodes and signal correlations, provides a comprehensive framework for neural activity modeling. The hybrid EEGNet-GAT model demonstrates substantial performance improvements, achieving classification accuracies of 92.13% on the N-Back task and 96.08% on MATB-II, significantly surpassing the results from standalone EEGNet. These findings underscore the potential of the EEGNet-GAT architecture for real-time cognitive workload estimation, offering strong applications in BCIs and neuroergonomic environments.