| # EEG Foundation-Model probing |
|
|
| Goal: reuse a **frozen** pretrained EEG backbone and fit only a light **linear/MLP |
| probe** on labelled MI data — i.e. no full retraining. This is how we'll test the |
| 32-ch cap data later, and it's how we get an apples-to-apples comparison against |
| the pyRiemann baselines. |
|
|
| ## Where things run |
| - **M5 Mac (MPS):** fine for *inference / linear-probing* of the small models |
| (EEGPT ≈ 10M params, CBraMod). Use `device="mps"`. |
| - **GPU server:** needed only for *fine-tuning the backbone* or *pretraining*. |
|
|
| ## Setup |
| ```bash |
| bash src/foundation/setup_foundation.sh # clone repos + fetch weights |
| ``` |
| Repos land in `third_party/`, weights in `checkpoints/`. |
|
|
| ## Candidates (all channel-flexible, weights public) |
| | Model | Repo | Input it expects | License | |
| |---------|--------------------------|-----------------------------|---------| |
| | EEGPT | `BINE022/EEGPT` | 58ch/256Hz/4s, patch 64 | see repo | |
| | CBraMod | `wjq-learning/CBraMod` | (ch, seg, 200 pts/patch)=200Hz | MIT | |
| | LaBraM | `935963004/LaBraM` | 200Hz, patch, channel names | see repo | |
|
|
| ## Probe recipe (identical across models) |
| 1. Pull MI epochs from MOABB (same `LeftRightImagery` paradigm as the baseline) |
| so the comparison is fair. |
| 2. Resample to the model's native rate (EEGPT 256 Hz, CBraMod/LaBraM 200 Hz); |
| map our channel names to the model's channel embedding. |
| 3. Forward pass through the **frozen** backbone → per-trial embedding. |
| 4. Fit `LogisticRegression` (or a 1-layer MLP) on embeddings; evaluate |
| cross-subject exactly like the baseline. |
|
|
| `probe_moabb.py` implements this loop; encoders live in `encoders.py`. |
|
|
| ## Status (cross-subject 2a, ROC-AUC — see ../../results/RESULTS.md) |
| | encoder | wired? | 2a AUC | notes | |
| |---|---|---|---| |
| | `raw` | ✅ | 0.521 | floor | |
| | `cbramod` | ✅ (MPS) | 0.560 | weights in `checkpoints/CBraMod/` | |
| | `labram` | ✅ (MPS) | 0.595 | weights ship in `third_party/LaBraM/checkpoints/` | |
| | `eegpt` | ⬜ | — | needs the figshare checkpoint (below) | |
|
|
| Run: `python src/foundation/probe_moabb.py --dataset 2a --subjects 9 --encoder labram --fmin 0.5 --fmax 45` |
| (set `PYTORCH_ENABLE_MPS_FALLBACK=1` for the transformer ops MPS doesn't cover). |
|
|
| All frozen probes underperform the classic Riemannian baseline (~0.78) — expected: MI |
| is a subtle *spatial* ERD contrast that a frozen generic backbone doesn't expose linearly. |
| FMs need **fine-tuning** (not frozen probing) to compete. |
|
|
| ### Gotchas already handled (in encoders.py) |
| - **Pool over time only, keep channels** — averaging over channels drives MI to chance. |
| - **Scale = µV/100** (`scale=0.01`); MOABB returns µV-scale here. Feed **broadband** 0.5–45 Hz. |
| - **LaBraM + timm 1.x**: `_timm1x_shim()` aliases the old `timm.models.layers/.registry`. |
| - **PyTorch 2.6 `torch.load`**: LaBraM ckpt needs `weights_only=False` (trusted repo file). |
|
|
| ### EEGPT (not yet wired — needs manual download) |
| Checkpoint is on figshare (blocked from automated fetch): |
| https://figshare.com/s/e37df4f8a907a866df4b → `eegpt_mcae_58chs_4s_large4E.ckpt` |
| (58 ch, **256 Hz**, 4 s, patch 64). Download it into `checkpoints/`, then mirror |
| `third_party/EEGPT/downstream/linear_probe_EEGPT_BCIC2A.py` (`EEGPTClassifier` in |
| `downstream/Modules/models/EEGPT_mcae_finetune.py`) as an `EEGPTEncoder`. Note its native |
| rate is 256 Hz — run the probe with `--sfreq 256`. |
|
|
