研究人员正在开发新的方法来解码脑电图（EEG）信号中的视觉信息，旨在改进脑机接口。一种名为“思维原子”（Atoms of Thought）的方法，将微状态作为大脑活动的离散构建块，以创建通用的脑电图表示，在睡眠分期和情绪识别等任务上优于传统方法。另一种方法STAMBRIDGE采用了一个包含频谱-时间调制和语义桥接的两阶段框架，以实现脑电图视觉解码的稳定跨模态对齐，展示了强大的零样本检索性能。第三篇论文提出了一种受神经科学启发的、分阶段的表示学习框架，将脑电图视觉解码分解为不同的阶段，解耦粗粒度和细粒度语义，以实现更有效的视觉解码。
AI
arXiv:2605.25605v1 Announce Type: cross Abstract: In the past decade, numerous studies have applied deep neural networks (DNNs) to decode auditory attention (AAD) from Electroencephalogram (EEG) signals via stimulus reconstruction. However, the influence of dataset balance on the…
arXiv cs.CL
TIER_1English(EN)·Zexuan Chen, Sichao Liu, Runhao Lu, Huichao Qi, Alexandra Woolgar, Xi Vincent Wang, Lihui Wang·
arXiv:2605.24523v1 Announce Type: cross Abstract: Visual decoding from brain signals is a key challenge at the intersection of computer vision and neuroscience, requiring methods that bridge neural representations and computational models of vision. We introduce a tri-modal contr…
Learning universal representations from electroencephalogram (EEG) signals is a cutting-edge approach in the field of neuroinformatics and brain-computer interfaces (BCIs). Conventionally, EEG is treated as a multivariate temporal signal, where time- or frequency-domain features …
Learning universal representations from electroencephalogram (EEG) signals is a cutting-edge approach in the field of neuroinformatics and brain-computer interfaces (BCIs). Conventionally, EEG is treated as a multivariate temporal signal, where time- or frequency-domain features …
arXiv cs.CV
TIER_1English(EN)·Jiaxiang Liu, Jiawei Du, Xupeng Chen, Guoqi Li, Jiang Cai, Simon Fong, Mingkun Xu·
arXiv:2605.24679v1 Announce Type: new Abstract: Cross-subject brain-to-visual decoding remains a core challenge in brain-computer interfaces due to severe inter-individual variability that induces systematic subject-specific functional misalignment. To address this issue, we prop…
arXiv:2605.23137v1 Announce Type: cross Abstract: Electroencephalography (EEG) visual decoding remains challenging due to the modality gap between low-SNR neural signals and highly structured vision--language spaces, making direct cross-modal alignment unstable. To address this, …
arXiv:2605.16923v3 Announce Type: replace Abstract: Decoding visual information from electroencephalography (EEG) signals remains a fundamental challenge in brain-computer interfaces and medical rehabilitation. Existing EEG visual decoding methods mainly focus on learning a singl…
