BrainOmni is a pretrained foundation model for EEG and MEG, released in tiny and base sizes, with a BrainTokenizer front end; its paper's appendix lists the EEG and MEG recordings it was pretrained on. BCI Report ran Base frozen on six of the eight core protocols: its tokenizer needs two-second windows, so the two one-second ERP protocols were not run rather than padded with invented samples. It keeps its upstream common average reference on the small layouts here.
BrainOmni Base: not run, never a zero. BrainTokenizer needs a 512-sample window (2.0 s at 256 Hz); the published segment is 1 s = 256 samples, so upstream unfold() would zero-pad 256 of 512 samples of every window (invented samples). All upstream downstream tasks use >= 2 s windows.
BrainOmni Base: not run, never a zero. BrainTokenizer needs a 512-sample window (2.0 s at 256 Hz); the published segment is 1 s = 256 samples, so upstream unfold() would zero-pad 256 of 512 samples of every window (invented samples). All upstream downstream tasks use >= 2 s windows.
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.
BrainOmni Base
Panel
A row beside the core matrix on every protocol page
Permissive weights licence; aggregate scores only, no weights redistributed.
Row footnote
p300-target and semantic-target not run: the 2 s tokenizer window cannot be met by 1 s segments without zero padding. Upstream common average reference kept on 4-8 channel montages.
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.
BCI Report