脑电图基础模型面临偏见、基准测试和临床效用的审查 · 跟踪3个来源

报道来源 [4]

1. arXiv cs.AI TIER_1 English(EN) · Yangxuan Zhou, Sha Zhao, Yuning Chen, Chen Wu, Jiquan Wang, Shijian Li, Gang Pan · 2026-08-06 04:00

   BrainBench：用于全面理解脑电图的大型语言模型基准测试

   arXiv:2608.04156v1 Announce Type: new Abstract: Electroencephalography (EEG) analysis extends beyond assigning predefined labels to recordings; it requires workflows connecting natural-language instructions, signal processing, quantitative evidence, and scientific interpretation.…

2. arXiv cs.LG TIER_1 English(EN) · Junjie Yu, Zihan Deng, Jianyu Zhang, Junrong Mu, Jiahui An, Wenxiao Ma, Ziling Lu, Yue Wang, Yan Zhu, Kexin Lou, Quanying Liu · 2026-08-04 04:00

   理解和纠正EEG基础模型中的低频偏差

   arXiv:2608.01898v1 Announce Type: new Abstract: Increasing EEG pretraining data scale or model capacity does not consistently improve downstream performance. We identify a persistent low-frequency bias in representations learned by diverse EEG foundation models, which remains acr…

3. arXiv cs.LG TIER_1 English(EN) · Dingkun Liu, Yuheng Chen, Zhu Chen, Zhenyao Cui, Yaozhi Wen, Jiayu An, Jingwei Luo, Dongrui Wu · 2026-08-04 04:00

   EEG-FM-Compass：脑电图基础模型的进展、基准测试和未来方向

   arXiv:2601.17883v3 Announce Type: replace Abstract: Electroencephalography (EEG) foundation models (FMs) have recently emerged as a promising paradigm for brain-computer interfaces, aiming to learn transferable neural representations from large-scale heterogeneous recordings. Des…

4. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Marzieh Zare · 2026-07-27 14:59

   EEG基础模型编码了什么：数据集身份与临床基准的阴性对照套件

   Pretrained EEG foundation models are proposed for clinical decoding, but whether reported gains transfer across populations or survive negative controls is unclear. We benchmark LaBraM, EEGMamba, CBraMod, REVE, LEAD, BENDR, and BIOT on five clinical tasks across four datasets. Pr…