EEGMamba is the Mamba-based EEG encoder from the CBraMod group (Wang et al., Neural Networks, 2025), not the unrelated model of the same name on arXiv (2407.20254). Its official code lists five pretraining sources, including TUEG and the Siena Scalp EEG Database; the paywalled paper was not read. BCI Report ran the released checkpoint frozen, pooled as the published CBraMod rows are, and adapted it on EEGMAT.
What was run, as the v9 release records it: each checkpoint’s revision and SHA-256, its encoder parameters, the footnote its rows carry and its weights licence. No weights, adapted weights or LoRA deltas were kept or shared, and no endorsement by the model’s authors is implied.
EEGMamba
Panel
A row beside the core matrix on every protocol page
Permissive weights licence; aggregate scores only, no weights redistributed.
Row footnote
Published CBraMod pooling; LoRA on Mamba2 in_proj and out_proj (24 targets).
EEGMAT adaptation
Run: a head trained on the frozen encoder against LoRA rank 4, three seeds.
Pretraining exposure, by dataset
Whether each core dataset is in the model authors’ published pretraining list, checked on 2026-10-04 and linked to the source. A dataset absent from the list is not proof that its recordings were never seen; recording-level audits were not done.
Note on the check: EEGMamba's list comes from the official code (five entries, matching the abstract); the paywalled paper was not read, so confidence is medium.
Cite this page
BCI Report (2026). EEGMamba: results on public EEG datasets. https://bci.report/methods/eegmamba/
Figures from release foundation-models-update-20261004 (2026-10-04). Cite the upstream datasets as well: each dataset’s page gives its credit.
BCI Report