NeuroGraph-TSC: A Neuro-Inspired Graph-Based Temporal-Spatial Classifier for Cognitive State Prediction from EEG

Accurate prediction of cognitive states such as psychological stress from electroencephalography (EEG) remains a significant challenge due to the inherently spatiotemporal and nonlinear nature of brain dynamics. To address these complexities, we propose NeuroGraph-TSC, a novel neuro-inspired, graph-based temporal-spatial classifier that incorporates domain-specific neuroscientific priors into a deep learning architecture for improved cognitive state decoding. The model constructs a spatial graph where EEG electrodes are represented as nodes, and inter-node edge weights are determined based on either scalp geometry or empirical functional connectivity, enabling physiologically meaningful spatial feature propagation. Temporal modeling is achieved through recurrent processing that captures both rapid and slow neural fluctuations. To further enhance biological plausibility, we integrate a neural mass model-based regularizer into the loss function, specifically adopting the Jansen-Rit dynamical system to constrain the model toward biophysically informed temporal dynamics.We evaluate NeuroGraph-TSC on the SAM-40 raw EEG stress dataset, achieving high classification performance across low, moderate, and high stress levels. Comprehensive ablation studies and interpretability analyses confirm the individual and collective contributions of the neuroscience-aligned components, validating both the robustness and neurophysiological relevance of the model. NeuroGraph-TSC offers a promising step toward bridging computational neuroscience and deep learning for advancing EEG-based affective computing.