Accurate visual decoding can reveal how the brain represents visual information and recover perceived content from neural signals such as electroencephalography (EEG), with potential for neural communication. However, current EEG-to-image retrieval methods perform far below their within-subject counterparts for new use...
Zhenyao Cui, Siyuan Kan, Siyang Li et al.· 1 citation
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.
Zhenyao Cui, Siyuan Kan, Ding-Kun Liu et al.· 0 citations
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
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.
Dingkun Liu, Yu-Heng Chen, Zhu Chen et al.· National Science Review· 12 citations· ⚡3
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.
Zhu Chen, Dingkun Liu, Yu-Heng Chen et al.· 0 citations
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...
Linhua Cong, Dingkun Liu, Dongrui Wu· 0 citations
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