arXiv Machine Learning
EEGDash Corpus: A Training-Ready Public Corpus for EEG Foundation Models
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NOMAD-EEG: Native Omni-Montage Adaptive Diffusion
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 Machine Learning
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NOMAD-EEG: Native Omni-Montage Adaptive Diffusion
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.
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BrainBench: Benchmarking Large Language Models for Comprehensive EEG Understanding
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.
arXiv Machine Learning
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OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models
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 AI
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Controlled Evaluation of EEG Foundation Models: Random Initialisation, Band-Power and Validation-Selected Probes
The paper evaluates five publicly released EEG foundation models and an in‑house JEPA‑style model using a protocol that includes three key controls: an untrained encoder, spectral band‑power features, and a probe layer selected without test‑set look‑ahead. The study finds that selecting the probe layer by test score can modestly improve peak performance and alter the peak‑versus‑output relationship, that random initialization can match or surpass pretraining on certain tasks, and that full fine‑tuning rarely yields gains beyond seed variability. These findings lead the authors to propose four new reporting practices and to release all experimental results for re‑analysis.
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EEG Benchmarking Needs a Task Specification Layer: NeuroDoc for Rulebook-Guided, Executable Benchmark Construction
Electroencephalography (EEG) foundation models increasingly rely on multi-dataset training and evaluation, yet public EEG datasets still lack a shared task specification layer that can turn heterogeneous recordings into reusable benchmark units. Existing standards organize files, metadata, and provenance, but they do not specify EEG tasks under a common language and rulebook, leaving critical task semantics scattered across papers, code, and manual interpretation.