MSCGC-KAN: Multi-scale Causal Graph Convolution and KAN-inspired Analytic-basis Mapping for EEG Emotion Recognition
Electroencephalogram (EEG)-based emotion recognition is an important affective computing task, and recent EEG foundation models provide useful generic representations for downstream adaptation. However, under the fine-tuning setting, three limitations remain prominent: insufficient modeling of multi-scale emotional dynamics, inadequate exploitation of inter-channel functional connectivity, and the limited expressive power of simple linear classification heads. To address these issues, this paper proposes a new EEG emotion recognition method, termed MSCGC-KAN, which introduces a structured task head composed of multi-scale causal graph convolution and Kolmogorov--Arnold feature mapping. Bu...
| Authors | Haoliang Gong, Qingshan She, Jiale Xu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 May 2026 |
| Last updated at source | 28 August 2026 |
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