发表机构
University of Illinois Urbana–Champaign; Carle Foundation Hospital（伊利诺伊大学厄巴纳-香槟分校; 卡尔基金会医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出轻量级脑电图基础模型USE-FM，通过自监督重建学习可迁移语义表征，以146万参数在冻结迁移下于异常检测和癫痫识别任务中达到与大型模型相当的性能。
AI 中文摘要
大规模脑电图基础模型已在多种神经系统疾病中展现出良好的迁移能力，但通常需要数百万参数和大量计算资源。本文提出了通用语义脑电图基础模型（USE-FM），一种轻量级脑电图基础模型，通过在坦普尔大学医院脑电图语料库（TUEG）上进行自监督信号重建来学习可迁移的神经表征。预训练后，编码器被冻结，并在两个临床不同的下游任务上评估：异常脑电图检测（TUAB）和癫痫发作识别（TUEP），采用统一的冻结迁移协议，与近期脑电图基础模型（包括LUNA-Base和CBraMod）进行比较。USE-FM仅需146万参数，约为现有模型的五分之一，取得了具有竞争力的整体性能，包括在TUEP上较强的敏感性和F1分数（敏感性$75.00 \pm 14.14$，F1 $70.37 \pm 4.01$），同时在TUAB上保持竞争力（AUC $85.24 \pm 5.61$）。除下游分类外，使用$k$-均值聚类结合PCA和t-SNE的潜在表征分析表明，USE-FM学习了与显著更大的基础模型相当的有组织的语义脑电图表征。这些结果表明，大规模自监督预训练使轻量级架构能够学习可迁移的语义脑电图表征，为跨疾病分析和未来神经系统疾病的临床决策支持提供了计算高效的基礎。
英文摘要
Large-scale EEG foundation models have demonstrated promising transferability across neurological disorders, but often require millions of parameters and substantial computational resources. In this paper, we present the Universal Semantic EEG Foundation Model (USE-FM), a lightweight EEG foundation model that learns transferable neural representations through self-supervised signal reconstruction on the Temple University Hospital EEG Corpus (TUEG). After pretraining, the encoder is frozen and evaluated on two clinically distinct downstream tasks, abnormal EEG detection (TUAB) and epileptic seizure recognition (TUEP), using a unified frozen-transfer protocol against recent EEG foundation models, including LUNA-Base and CBraMod. With only 1.46 million parameters, approximately one-fifth the size of existing models, USE-FM achieves competitive overall performance, including strong sensitivity and F1-score on TUEP (SEN $75.00 \pm 14.14$, F1 $70.37 \pm 4.01$), while maintaining competitive performance on TUAB (AUC $85.24 \pm 5.61$). Beyond downstream classification, latent representation analysis using $k$-means clustering together with PCA and t-SNE demonstrates that USE-FM learns organized semantic EEG representations comparable to substantially larger foundation models. These results suggest that large-scale self-supervised pretraining enables lightweight architectures to learn transferable semantic EEG representations, providing a computationally efficient foundation for cross-disorder analysis and future clinical decision support in neurological disorders.
