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[2602.22981] RepSPD: Enhancing SPD Manifold Representation in EEGs via Dynamic Graphs
Summary
The paper introduces RepSPD, a novel geometric deep learning model that enhances the representation of EEG data through dynamic graphs, improving robustness and generalization in decoding brain activity.
Why It Matters
This research addresses limitations in current EEG analysis methods by incorporating geometric learning and dynamic graph features, which could lead to more accurate interpretations of brain activity. This has significant implications for neuroscience and clinical applications, potentially improving diagnostic and therapeutic strategies.
Key Takeaways
- RepSPD utilizes a cross-attention mechanism on Riemannian manifolds to enhance EEG data representation.
- The model introduces a global bidirectional alignment strategy to reduce geometric distortions.
- Extensive experiments show RepSPD outperforms existing EEG representation methods in robustness and generalization.
Computer Science > Artificial Intelligence arXiv:2602.22981 (cs) [Submitted on 26 Feb 2026] Title:RepSPD: Enhancing SPD Manifold Representation in EEGs via Dynamic Graphs Authors:Haohui Jia, Zheng Chen, Lingwei Zhu, Xu Cao, Yasuko Matsubara, Takashi Matsubara, Yasushi Sakurai View a PDF of the paper titled RepSPD: Enhancing SPD Manifold Representation in EEGs via Dynamic Graphs, by Haohui Jia and 6 other authors View PDF Abstract:Decoding brain activity from electroencephalography (EEG) is crucial for neuroscience and clinical applications. Among recent advances in deep learning for EEG, geometric learning stands out as its theoretical underpinnings on symmetric positive definite (SPD) allows revealing structural connectivity analysis in a physics-grounded manner. However, current SPD-based methods focus predominantly on statistical aggregation of EEGs, with frequency-specific synchronization and local topological structures of brain regions neglected. Given this, we propose RepSPD, a novel geometric deep learning (GDL)-based model. RepSPD implements a cross-attention mechanism on the Riemannian manifold to modulate the geometric attributes of SPD with graph-derived functional connectivity features. On top of this, we introduce a global bidirectional alignment strategy to reshape tangent-space embeddings, mitigating geometric distortions caused by curvature and thereby enhancing geometric consistency. Extensive experiments demonstrate that our proposed framework significan...
