NOMAD-EEG is a 12‑million‑parameter diffusion model that learns EEG representations independent of electrode number, identity, or recording length by using 3D scalp positions and state‑space layers over time. Trained on 147 h of unlabelled EEG from a single corpus, it outperformed seven larger foundation models across ten datasets, achieving superior accuracy on tasks such as seizure detection, epilepsy diagnosis, motor imagery, and P300 detection. The model’s compact size and single‑corpus training yet strong performance demonstrate the feasibility of a versatile, lightweight EEG foundation model.
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...
arXiv:2607. 21384v1 Announce Type: new Abstract: Electroencephalography (EEG) models used for epilepsy are often limited to specific datasets and tasks.
NeuroAtlas is the largest EEG benchmark to date, comprising 42 datasets and 260,000 hours of clinical EEG data across epilepsy, sleep medicine, brain age estimation, and brain‑computer interfaces. The study evaluates foundation models (FMs) for EEG against supervised baselines and generic time‑series FMs, finding that EEG‑specific FMs do not consistently outperform generic ones. It also demonstrates that standard machine‑learning metrics are inadequate for clinical relevance, advocating for task‑specific measures such as event‑level decision quality, hypnogram features, and brain‑age gap.
arXiv:2611.06356v1 Announce Type: new Abstract: Foundation models for electroencephalography (EEG) are pretrained on tens of thousands of hours of recordings, and their accuracy grows with that amoun...
arXiv:2606. 00815v1 Announce Type: new Abstract: Electroencephalography (EEG) supports a variety of brain-computer interface (BCI) tasks ranging from brain-state monitoring to human-LLM interactions.
arXiv:2608. 13676v1 Announce Type: new Abstract: Objective: Foundation models represent the next advancement in AI for EEG analysis; however current explainable AI techniques provide attribution scores in the time-channel input space, which is mismatched to clinical intuition about EEG.
The study introduces low‑voltage intervals (LVIs) as an amplitude‑ and duration‑based marker of neonatal EEG discontinuity and develops a ConvNeXt AI model that detects LVIs with expert‑level accuracy (AUC = 0.996, MCC = 0.926). In a cohort of 444 neonates, LVI percentage strongly correlates with EEG background grades, moderately with hypoxic‑ischaemic encephalopathy severity, and predicts seizures (AUC = 0.872). The results demonstrate that automated LVI quantification can serve as a robust biomarker for neonatal encephalopathy and seizure risk.
arXiv:2607. 24519v1 Announce Type: cross Abstract: Pretrained EEG foundation models are increasingly proposed for clinical decoding, but their transfer across populations and robustness to negative controls remain unclear.
Pretrained EEG foundation models are increasingly proposed for clinical decoding, but their transfer across populations and robustness to negative controls remain unclear. We benchmark six models (LaBraM, EEGMamba, CBraMod, REVE, BENDR, and BIOT) on five clinical tasks across four datasets using frozen linear probes with leave-one-subject-out, subject-grouped, or explicitly identified recording-level splits.
NeurDuo-EEG is a causal EEG foundation model that introduces channel‑resolved persistent memory and multi‑timescale memory management, allowing it to model continuous EEG with a fixed‑size state. Trained on 3,955 hours of EEG from 17 public datasets, it outperforms baselines on most short‑window and long‑sequence tasks, notably improving seizure detection AUC‑PR from 0.285 to 0.471. The Small variant achieves these gains with only 4.7 M parameters and supports efficient streaming inference with constant per‑chunk latency even as history grows to one hour.
arXiv:2609.36087v1 Announce Type: cross Abstract: Clinicians and neuroscientists have long analyzed intracranial electroencephalography (iEEG) through directly measurable physiological characteristic...
arXiv:2607. 22733v1 Announce Type: cross Abstract: We investigate whether a generative model can supply useful synthetic motor-imagery (MI) electroencephalography (EEG) trials that improve the accuracy of independent downstream classifiers.