CFSDBN: Emotion Recognition via Channel Feature Selection and Dynamic Brain Network

The functional connectivity patterns revealed by emotion recognition closely align with neural pathways involved in emotional processing. Traditional methods often fail to adequately integrate the multidimensional spatiotemporal-spectral characteristics of Electroencephalography (EEG), making it challenging to accurately characterise dynamic emotional processes. Predefined brain regions and fixed thresholds yield coarse functional networks, which limit the accurate identification of critical connections. Furthermore, black-box models lack interpretability, providing little decision support or visualisable evidence of neural circuits for neuroscience and clinical applications. To address these limitations, this study proposes an emotion recognition via channel-feature selection and dynamic brain network (CFSDBN). First, spatial-spectral features are extracted using a residual network, while bidirectional gated recurrent units capture temporal dynamics, thereby improving feature utilisation. Next, these spatio-temporal-spectral features serve as node inputs to a graph attention network, where node attention weights enable adaptive channel selection and sparse functional connectivity learning, thereby overcoming localisation inaccuracies caused by coarse-grained processing. Finally, joint node embeddings and connection weights are used for emotion classification, and key channels and neural circuits are visualised to provide interpretable evidence for emotional neural mechanisms. By deeply coupling multidimensional features with brain network optimisation, CFSDBN achieves significant improvements in classification performance on the DEAP, MODMA, and SEED-V datasets. It enhances hierarchical interpretability from micro-level features to macro-level network interactions, offering a high-performance and explainable solution for EEG-based emotion recognition.