ERP-FM：面向通用ERP分析的基础模型
ERP-FM: A Foundation Model for Universal ERP Analysis
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- University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
- Johns Hopkins University（约翰斯·霍普金斯大学）
- Dartmouth College（达特茅斯学院）
- University of Florida（佛罗里达大学）
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中文总结 AI 辅助
本研究提出ERP-FM，首个专用于ERP分析的基础模型，利用大规模单试次预训练，在12个下游数据集上超越17种方法，并证明单试次与平均试次ERP互补整合可显著提升疾病分类性能。
中文摘要 AI 辅助
基础模型近期在学习可泛化的脑电图（EEG）表征方面展现出强大潜力，但其在事件相关电位（ERP）分析中的有效性仍不明确。在本工作中，我们探究两个基本问题：1）基础模型学习能否惠及ERP分析，以及现有EEG基础模型向ERP任务迁移的局限性何在？2）单试次与平均试次ERP的互补优势能否整合到统一的训练流程中？为研究这些问题，我们构建了一个大规模ERP语料库，包含来自38个数据集、18种范式、3696名受试者的1,517,157个单试次ERP。利用该语料库，我们提出了ERP-FM，据我们所知，这是首个专门针对ERP表征学习开发的基础模型。ERP-FM采用单通道分词、时间和空间位置嵌入以及混合掩码自编码，用于大规模单试次预训练。我们将模型与17种现有方法在12个下游数据集上进行比较，涵盖ERP事件/条件分类和神经系统疾病分类。我们的模型在所有评估方法中取得了最佳总体平均排名。此外，我们的分析揭示，ERP和非ERP EEG预训练均可为ERP任务提供可迁移表征，而细粒度时间分词对于有效建模瞬态ERP动态至关重要。我们进一步发现，单试次和平均试次ERP发挥互补而非竞争作用。将单试次预训练与平均试次下游适配相结合，显著改善了神经系统疾病分析。总体而言，这些发现为ERP分析建立了一条有效的基础模型训练流程，并代表了向可泛化ERP表征学习迈出的重要进展。源代码：此HTTPS URL。
英文摘要
Foundation models have recently shown strong potential for learning generalizable EEG representations, yet their effectiveness for event-related potential (ERP) analysis remains unclear. In this work, we investigate two fundamental questions: 1) can foundation-model learning benefit ERP analysis, and what limits the transfer of existing EEG foundation models to ERP tasks? 2) can the complementary advantages of single-trial and averaged-trial ERP be integrated into a unified training pipeline? To study these questions, we curate a large-scale ERP corpus comprising 1,517,157 single-trial ERPs from 3,696 subjects across 38 datasets and 18 paradigms. Leveraging this corpus, we introduce ERP-FM, to the best of our knowledge, the first foundation model specifically developed for ERP representation learning. ERP-FM uses single-channel tokenization, temporal and spatial positional embeddings, and mixed masked autoencoding for large-scale single-trial pretraining. We compare our model against 17 existing methods on 12 downstream datasets covering ERP event/condition classification and neurological disease classification. Our model achieves the best overall average rank across all evaluated methods. Furthermore, our analyses reveal that both ERP and non-ERP EEG pretraining can provide transferable representations for ERP tasks, while fine-grained temporal tokenization is critical for effectively modeling transient ERP dynamics. We further find that single-trial and averaged-trial ERP play complementary rather than competing roles. Combining single-trial pretraining with averaged-trial downstream adaptation substantially improves neurological disease analysis. Overall, these findings establish an effective foundation-model training pipeline for ERP analysis and represent significant progress toward generalizable ERP representation learning. Source code: https://github.com/DL4mHealth/ERP-FM
