arXiv cs.AI· Hanseul Choi, Jinyeong Park, Seongwon Jin, Sungho Park, Jibum Kim·· 14 小时前AI 评分26
CAMEL-CLIP：面向通用脑基础模型的通道感知多模态 EEG-文本对齐
CAMEL-CLIP: Channel-aware Multimodal Electroencephalography-text Alignment for Generalizable Brain Foundation Models
AI 导读
CAMEL-CLIP 是一个对异构通道配置鲁棒的对比式 EEG-文本多模态基础模型，由通道属性位置编码、动态通道投影和双层对比学习三部分组成。在线性探测下，它在多个数据集上取得所评估基线中最高的平均平衡准确率，并在不同通道配置间迁移时无需重新训练分类器即可保持较高分类性能。
正文 · 原文
Abstract:Electroencephalography (EEG) foundation models have shown promise for learning generalizable representations, yet they remain sensitive to channel heterogeneity, such as changes in channel composition or ordering. We propose channel-aware multimodal EEG-text alignment contrastive language image pretraining (CAMEL-CLIP), a contrastive EEG-text multimodal foundation model designed to be robust to heterogeneous channel configurations and widely applicable to diverse downstream tasks. CAMEL-CLIP combines three components: (1) channel attribute-based positional encoding, which identifies channels using their anatomical attributes and reference type; (2) dynamic channel projection, which preserves channel-specific information across varying numbers of EEG channels and combines textual reports with channel information to support channel-wise EEG-text matching; and (3) dual-level contrastive learning, which aligns EEG signals with text at both the individual-channel and whole-sample levels. Experimental results show that CAMEL-CLIP achieves the highest mean balanced accuracy among the evaluated baselines across multiple datasets under linear probing. CAMEL-CLIP also shows high classification performance when transferred between different channel configurations without classifier retraining.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.13272 [cs.LG] |
| (or arXiv:2603.13272v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.13272 arXiv-issued DOI via DataCite |
Submission history
From: Jibum Kim [view email]
[v1]
Fri, 27 Feb 2026 05:05:08 UTC (3,096 KB)
[v2]
Sun, 25 Oct 2026 23:51:55 UTC (11,891 KB)
来源：arXiv cs.AI · arxiv.org