Card: list the training data (datasets:, base_model:) and add a Training data section

Follow-up to braindecode PR #1493: `from_pretrained` reads `datasets:` into `model.card_data`. Ids: MOABB names, NEMAR nm/on ids, OpenNeuro ds ids; evidence per checkpoint.

README.md

@@ -6,6 +6,17 @@ tags:
 - eeg
 - foundation-model
 license: mit
+datasets:
+- nm000245
+- nm000172
+- nm000139
+- nm000135
+- Kaya2018
+- Won2022
+- nm000129
+- nm000339
+- SparrKULee
+- SEED-V
 ---
 
 # STEEGFormer (base)
@@ -30,6 +41,34 @@ ViT-MAE EEG foundation model — braindecode port of **ST-EEGFormer** (base vari
 
 Channel positions are resolved from electrode names in `chs_info` (145-slot shared montage vocabulary).
 
+<!-- braindecode:training-data -->
+## Training data
+
+The data that shaped these weights. The ids are the `datasets:` field of this card,
+which braindecode reads into `model.card_data.datasets` when you call
+`from_pretrained` (MOABB class names for MOABB datasets, OpenNeuro / NEMAR
+accessions, Hub ids otherwise). `nm`/`on` ids are datasets on [NEMAR](https://nemar.org)
+(`on` = NEMAR's copy of the OpenNeuro dataset with the same number). Rows marked
+`private:` are data the authors did not release; they are not in `datasets:`.
+
+| `datasets:` id | Dataset | Stage | Modality |
+|---|---|---|---|
+| [`nm000245`](https://nemar.org/dataset/nm000245) | [EEG-MI-BCI: Cho et al. 2017, EEG datasets for motor imagery BCI (52 subjects, 64 ch, left/right-hand MI; GigaDB 100295); MOABB Cho2017](http://gigadb.org/dataset/100295) | pretraining | EEG |
+| [`nm000172`](https://nemar.org/dataset/nm000172) | [High Gamma Dataset (HGD), Schirrmeister et al. 2017 (20 subjects, 128 ch, executed movements); MOABB Schirrmeister2017](https://gin.g-node.org/robintibor/high-gamma-dataset) | pretraining | EEG |
+| [`nm000139`](https://nemar.org/dataset/nm000139) | [BCI Competition IV dataset 2a (9 subjects, 22 ch, 4-class MI); MOABB BNCI2014_001](https://www.bbci.de/competition/iv/#dataset2a) | pretraining | EEG |
+| [`nm000135`](https://nemar.org/dataset/nm000135) | [BCI Competition IV dataset 2b (9 subjects, 3 ch, 2-class MI); MOABB BNCI2014_004](https://www.bbci.de/competition/iv/#dataset2b) | pretraining | EEG |
+| `Kaya2018` | [Kaya et al. 2018, A large electroencephalographic motor imagery dataset for EEG BCIs (Sci. Data 5:180211; Figshare collection 3917698). Paper uses two subsets, 'Large-MI-Classic' and 'Large-MI-5F'. MOABB Kaya2018 (moabb>=1.7.2) loads only the 17 CLA files, not the 5F (or HaLT) files](https://figshare.com/collections/A_large_electroencephalographic_motor_imagery_dataset_for_electroencephalographic_brain_computer_interfaces/3917698) (NEMAR [nm000137](https://nemar.org/dataset/nm000137) holds a subset of it) | pretraining | EEG |
+| `Won2022` | [Won et al. 2022, EEG dataset for RSVP and P300 speller BCIs (Sci. Data 9:388; 55 participants P300 speller, 50 RSVP; Figshare collection 5769449); not in MOABB](https://doi.org/10.6084/m9.figshare.c.5769449.v1) | pretraining | EEG |
+| [`nm000129`](https://nemar.org/dataset/nm000129) | [BETA: large benchmark database toward SSVEP-BCI application, Liu et al. 2020 (70 subjects, 40 targets); MOABB Liu2020BETA](https://doi.org/10.3389/fnins.2020.00627) | pretraining | EEG |
+| [`nm000339`](https://nemar.org/dataset/nm000339) | [Stieger et al. 2021, continuous sensorimotor-rhythm BCI learning in a large population (62 subjects, ~600 h, 62 ch; Figshare 13123148); MOABB Stieger2021](https://doi.org/10.6084/m9.figshare.13123148.v1) | pretraining | EEG |
+| `SparrKULee` | [SparrKULee: speech-evoked auditory response repository of KU Leuven (Bollens et al. 2023; 85 participants, 64 ch BioSemi); called 'KUL Auditory Decoding Dataset' in the paper](https://doi.org/10.48804/K3VSND) (NEMAR [nm000238](https://nemar.org/dataset/nm000238) holds a subset of it) | pretraining | EEG |
+| `SEED-V` | [SEED-V emotion EEG dataset (Liu et al. 2022; 16 subjects x 3 sessions, 62 ch, five emotions)](https://bcmi.sjtu.edu.cn/home/seed/seed-v.html) | pretraining | EEG |
+| `private:KU-Leuven-in-house-EEG` | In-house EEG recorded in the authors' lab (KU Leuven Laboratory for Neuro- & Psychophysiology), multiple paradigms (e.g. MI and SSVEP) and diverse montages; not released, not itemised | pretraining | EEG |
+
+- MAE self-supervised pre-training, 288 epochs (release note). MAE pre-training corpus of App. E.2 (pp. 25-26), '11 public datasets' + an in-house set, as the paper lists them: 1) EEG-MI-BCI (Cho et al., 2017) -> Cho2017; 2) HGD (Schirrmeister et al., 2017) -> Schirrmeister2017; 3) BCI-Comp-IV2a (Tangermann et al., 2012) -> BNCI2014_001 (official training session only; the official test session was the validation split, E.4 p.26); 4) BCI-Comp-IV2b (Tangermann et al., 2012) -> BNCI2014_004; 5) Large-MI-Classic and 6) Large-MI-5F (both Kaya et al., 2018) -> Kaya2018 (one dataset, two paradigm subsets); 7) P300 (Won et al., 2022: P300 speller + RSVP) -> Won2022; 8) SSVEP (Liu et al., 2020 = BETA) -> Liu2020BETA; 9) Online MI BCI Classification (Stieger et al., 2021) -> Stieger2021; 10) KUL Auditory Decoding Dataset (Bollens et al., 2023 = SparrKULee) -> SparrKULee; 11) SEED-V (Liu et al., 2022) -> SEED-V; plus in-house EEG recorded in the authors' lab (multiple paradigms, e.g. MI and SSVEP) -> private:KU-Leuven-in-house-EEG. Preprocessing: notch, 0.1-64 Hz band-pass, 128 Hz, per-channel z-score (E.3). Windows: 6 s / 0.5 s hop for continuous sets, 6 s / 2.5 s hop for the three large MI sets, 2 s / 0.125 s hop for SSVEP (E.4). Validation: BCI-Comp-IV2a official test set; 20% held out for every other set; >8M overlapping segments overall.
+- Sources: Yang et al., ICLR 2026, https://openreview.net/forum?id=5Xwm8e6vbh; PDF via Wayback snapshot http://web.archive.org/web/20260609032700/https://openreview.net/pdf?id=5Xwm8e6vbh App. E.2, p.25; https://github.com/LiuyinYang1101/STEEGFormer/releases/tag/ST-EEGFormer-base (release body).
+
+
 ## Usage
 
 ```python