The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The paper presents a diagnostic framework for Alzheimer’s disease that uses the Large Brain Model (LaBraM), a foundation model pretrained on over 2,500 hours of EEG data, to generate high‑dimensional latent embeddings. These embeddings are fed into a non‑linear Random Forest classifier, achieving an ROC‑AUC of 89.36% ± 3.49%, PR AUC of 81.45% ± 4.43%, and Balanced Accuracy of 82.44% ± 4.34% in a subject‑independent 5‑fold cross‑validation setting, using only 8‑second EEG segments. Post‑hoc occlusion and neurophysiological alignment analyses confirm that the model captures clinically validated biomarkers such as occipital‑frontal Alpha and Theta rhythm degradation and correlates with cognitive performance and clinical severity.
arXiv:2509. 17920v2 Announce Type: replace Abstract: Current deep learning models for electroencephalography (EEG) are often task-specific and depend on large labeled datasets, limiting their adaptability.
arXiv:2508. 17742v3 Announce Type: replace-cross Abstract: Electroencephalography foundation models (EEG-FMs) have advanced brain signal analysis, but the lack of standardized evaluation benchmarks impedes model comparison and scientific progress.
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.
arXiv:2607. 24519v3 Announce Type: replace-cross Abstract: EEG foundation-model gains may depend on cohort, montage, or probe design.