We have 8 of 35 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
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.
A neural-anchor-based retrieval framework that treats the high-repetition center as an anchor and approaches it from both sides: a denoiser pulls the noisy query toward the true anchor, and a small network predicts each candidate's pseudo anchor from its image and pulls the image toward it.
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.
The results indicate that: 1) linear probing is frequently insufficient; 2) specialist models trained from scratch remain competitive across many tasks; and 3) larger FMs do not necessarily yield better generalization performance under current data regimes and training practices.
STEAM is presented, a hierarchical transfer framework that reconciles general-purpose representation learning with paradigm-specific specialization in EEG foundation models and attains the best average rank among the compared methods at a competitive inference cost measured in FLOPs.
Shrinkage-Whitened Proxy Cross-Entropy (SW-ProxyCE), a query-free task-aware attack framework that recovers task-level decision geometry from a small labeled reference set through shrinkage-whitened class prototypes, enabling transferable adversarial generation without training an additional surrogate classifier is pro...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
