新设备布局下，用通道元信息提升脑电模型泛化能力
Device Passport: Enabling Spatio-Temporal Pretrained Models to Generalize Across Input Layouts
- 结合通道功能与元数据构建动态嵌入
- 在耳部脑电迁移中优于现有最强基线
- 适合跨设备脑电信号建模的研究者
新设备布局因缺乏大规模对应数据集而带来建模挑战。若生物信号基础模型能有效泛化到新布局，则可提供可行解决方案。为提升跨布局迁移性能，我们研究了不同通道嵌入技术在预训练布局与下游解码布局差异较大时的表现。提出 Device Passport，一种新型通道嵌入方法，通过整合每个通道的功能活动和元数据，学习专家模型与混合模型。这与以往仅使用功能信息或仅使用元数据来查找学习或固定位置嵌入的方法形成对比。在受控子集迁移实验和真实场景下的耳部脑电迁移中，Device Passport 整体表现具有竞争力，并在推动本工作动机的布局迁移场景中超越最强学习基线。结果表明，通道嵌入设计是复用大规模预训练生物信号模型于新设备时的关键考量。
原文摘要 · Abstract (English)
New device layouts pose a challenging modeling problem due to the lack of large datasets for each specific layout. Biosignal foundation models offer a plausible solution if they are able to generalize to new layouts effectively. To improve cross-layout transfer, we study how different channel embedding techniques behave when pretraining layouts differ substantially from the downstream decoding layout. We propose Device Passport, a new channel embedding technique that learns experts and mixture models that take each channel's functional activity and metadata as input. This contrasts with prior embedding methods, which typically use only functional information or only metadata to look up learned or fixed positional embeddings. Across controlled subset-transfer experiments and realistic transfer to ear-EEG, Device Passport is competitive overall and improves over the strongest learned baseline in the layout-transfer regimes that motivate this work. These results suggest that channel embedding design is a key consideration when reusing large-scale pretrained biosignal models on new devices.
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