CAMEL-CLIP：通道感知的 EEG-文本对齐，打造可泛化的脑电基础模型
CAMEL-CLIP: Channel-aware Multimodal Electroencephalography-text Alignment for Generalizable Brain Foundation Models
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研究者提出 CAMEL-CLIP，一个通道感知的 EEG-文本对比多模态基础模型，可应对脑电通道构成与顺序异质性问题。模型包含通道属性位置编码、动态通道投影和通道级与样本级双层对比学习三个组件。实验显示其在多数据集线性探测中取得最高平均平衡准确率，且在不同通道配置间迁移时无需重训练分类器仍保持高分类性能。
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