arXiv eess.SP· Abdulkader Helwan, Khadidja Henni, Lina Abou-Abbas·· 2 小时前AI 评分52
NOMAD-EEG：按电极 3D 位置建模的扩散式 EEG 基础模型
NOMAD-EEG: Native Omni-Montage Adaptive Diffusion
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研究团队提出 NOMAD-EEG，一个 12.0 M 参数的扩散模型，沿时间交替使用状态空间层、不做分块，并通过电极的 3D 头皮位置在电极间做注意力，从而不依赖电极数量、身份和顺序以及输入长度。
正文 · 原文
Abstract:Purpose. Electroencephalography (EEG) montages, channel counts and recording lengths vary widely, but most EEG foundation models are bound to the channel set and input length they were trained on. We aimed to build a generative EEG foundation model independent of the number, identity and order of electrodes and of the input length. Methods. NOMAD-EEG is a 12.0 M-parameter diffusion model that alternates state-space layers along time, without patches, with attention across electrodes identified only by their 3D scalp position. It was pre-trained on 147 h of unlabelled EEG from a single corpus and evaluated with frozen linear probes on ten datasets with 1 to 64 electrodes against seven foundation models run under one protocol, and on two unseen populations. Results. Despite its small size (12.0 M parameters against 69 M for REVE) and single pre- training corpus, NOMAD-EEG outperformed all seven foundation models on EEGMMIDB mo- tor imagery (balanced accuracy 0.442), Siena seizure detection (macro F1 0.622) and Bonn seizure detection (AUROC 0.999), had the highest accuracy of the seven compared models on TUEP epilepsy diagnosis (0.640) and, after fine-tuning, the highest \k{appa} (0.635) and weighted F1 (0.814) on TUEV event classification. It was the best of five models on P300 detection in autism with an unseen eight-electrode montage (AUROC 0.718). With only 5 of 19 electrodes, its mean AUROC fell only from 0.942 to 0.891. Conclusion. Identifying electrodes by position and modelling time without patches let one small model, pre-trained on one corpus, outperform much larger EEG foundation models on seizure detection, epileptiform event classification, epilepsy diagnosis and motor imagery.
| Comments: | 37 pages and 7 figures |
| Subjects: | Machine Learning (cs.LG); Signal Processing (eess.SP) |
| Cite as: | arXiv:2610.35246 [cs.LG] |
| (or arXiv:2610.35246v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35246 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Lina Abou-Abbas [view email]
[v1]
Mon, 26 Oct 2026 11:26:59 UTC (490 KB)
来源：arXiv eess.SP · arxiv.org