SingLEM is a single-channel EEG foundation model: a tokenizer encodes each channel on its own. Its paper lists 71 public pretraining datasets, TMNRED among them, so its cells on the semantic-target protocol are marked as in the authors' pretraining list. BCI Report ran the primary checkpoint, the one that excludes the authors' own downstream sources, frozen, and adapted it on EEGMAT. Its tokenizer does not see the last quarter-second of each two-second segment.
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.
SingLEM
Panel
A row beside the core matrix on every protocol page
Checkpoint committed in the MIT repository; aggregate scores only.
Row footnote
semantic-target is pretraining-exposed (TMNRED is in the SingLEM corpus). Each channel is encoded independently; the last 0.25 s of each 2 s segment is not seen by the tokenizer.
Notes
SingLEM tokenizer (128-sample tokens, stride 96 at 128 Hz) leaves the last 0.25 s of every 2 s segment unseen.
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