arXiv:2607. 03925v1 Announce Type: new Abstract: EEG foundation models have shown strong potential in learning generalized representations across subjects and tasks.
By Weibin Li, Wendu Li, Yushan You, Chen Wei, Quanying Liu
arXiv:2605.23137v3 Announce Type: replace-cross
Abstract: Electroencephalography (EEG) visual decoding remains challenging due to the modality gap between low-SNR neural signals and highly structured...
By Jiahe Meng, Weiming Zeng, Yueyang Li, Bo Chai, Hongjie Yan, Zhiguo Zhang, Wai Ting Siok, Nizhuan Wang
arXiv:2607. 18344v1 Announce Type: cross Abstract: EEG-based visual decoding provides a non-invasive pathway for interpreting visual semantics.
By Yiheng Liu, Chuhang Zheng, Peiliang Gong, Jingtao Liu, Daoqiang Zhang, Qi Zhu
arXiv:2607. 18344v2 Announce Type: replace-cross Abstract: EEG-based visual decoding provides a non-invasive pathway for interpreting visual semantics.
By Yiheng Liu, Chuhang Zheng, Peiliang Gong, Jingtao Liu, Daoqiang Zhang, Qi Zhu
arXiv:2606. 01767v1 Announce Type: new Abstract: Electroencephalography (EEG) is the cornerstone of non-invasive brain-computer interfaces (BCIs), yet conventional decoding relies on fragmented, task-specific architectures that severely limit cross-task scalability.
By Yangxuan Zhou, Sha Zhao, Jiquan Wang, Shijian Li, Gang Pan
The paper introduces CAIA, a framework that enhances EEG-based visual decoding by adaptively blurring visual features to match neural granularity and applying an information-constrained alignment on EEG signals. It incorporates cognitive-guided adaptive blurring, spectral sparsity regularization, and a distribution-aware boundary calibration loss to improve signal-to-noise ratio and alignment robustness. Experiments show CAIA outperforms previous methods in both subject-dependent and subject-independent zero-shot brain-to-image retrieval tasks.
By Fan Yin, Chuhang Zheng, Peiliang Gong, Donghai Guan, Qi Zhu