Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) achieve high information transfer rates but often require sufficient subject-specific calibration data for reliable decoding. Analytical spatial filters are effective with limited data, but their linear formulations constrain representa...
DenoFlow is proposed, which casts SSVEP denoising as transport: instead of learning a direct map from a contaminated trial to a clean one, a field network regresses the velocity of the straight path between them, following the rectified-flow formulation, and denoising integrates that field forward from the observation.
This work proposes Subject Coordinate Recovery (SCORE), a target label-free framework combining recovery-aware source training with coordinate alignment at deployment that brings brain-based visual decoding closer to robust, practical, low-latency deployment across users.
Zhenyao Cui, Siyuan Kan, Siyang Li et al.· 1 citation
RAMamba-Net is proposed, a reliability-aware Mamba-based multimodal fusion network for AAD that effectively exploits complementary EEG-EOG information, yielding accuracy gains over unimodal baselines, and is robust to signal perturbation and parameter variation.
CoMA-DiT is proposed, a bidirectional cross-modal Diffusion Transformer for latent augmentation that treats paired modalities as sources of mutual generative supervision rather than merely as inputs to be fused.
Zi-Wei Wang, Xing-Yi He, Hong-Bin Wang et al.· 0 citations
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