Geometric Positional Encoding and Dynamic Weight Fusion for EEG-fNIRS Emotion Recognition

Background/Objectives: Driven by the neurovascular coupling mechanism, electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) capture shared underlying neural activity from electrophysiological and hemodynamic perspectives and thus complement each other. Emotional processing relies on functional synergy across brain regions recorded via EEG and fNIRS channels. Functionally similar channels are co-activated and adjacent in functional space. Nevertheless, existing EEG–fNIRS emotion recognition methods characterize inter-channel synergy using physical distances or graph structures. They fail to quantify channel-relative positions in functional space and capture synergies between functionally similar channels. Furthermore, these methods overlook dynamic modality contributions during fusion, rendering predictions susceptible to low-quality signals. Methods: To tackle these limitations, we propose the Geometric Positional Encoding and Dynamic Weight Fusion Network (GDWF-Net). The EEG branch adopts functional-space geometric distances as positional cues and embeds them into attention via rotary positional encoding to model inter-channel functional synergy. The fNIRS branch extracts spatiotemporal features using lightweight modules. The fusion module computes dynamic weights from modality contributions, suppresses noisy low-quality modalities via negative learning, and models cross-modal interactions within a mixed-curvature space. Conclusions: Evaluated on the ENTER dataset, GDWF-Net achieves accuracies of 81.63% and 65.71% in subject-dependent and cross-subject settings, surpassing state-of-the-art baselines. Ablation experiments and visualizations verify the effectiveness and interpretability of each proposed module.