braindecode/megxl-pretrained

MEG-XL (pretrained, meg-xl-med-v2)

Weights of MEG-XL (Jayalath and Parker Jones, ICML 2026) for braindecode.models.MEGXL: the released meg-xl-med-v2.ckpt (step 113000, the checkpoint the authors' model card links) with its frozen BioCodec tokenizer, converted to braindecode's parameter names. The class is added by braindecode#1554 (braindecode 2.1).

from braindecode.models import MEGXL

model = MEGXL.from_pretrained(
    "braindecode/megxl-pretrained", chs_info=raw.info["chs"], n_outputs=2
)

The files hold no classification head: it is not pretrained and its shape depends on the number of channels, so from_pretrained builds a new one (PyTorch's default init) for your chs_info. Fine-tune or linear-probe before use.

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 (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
CamCAN	Cambridge Centre for Ageing and Neuroscience (Cam-CAN) MEG data, Shafto et al. 2014 / Taylor et al. 2017	pretraining	MEG
MOUS	Mother Of Unification Studies (MOUS), Schoffelen et al. 2019, Donders Repository DSC_3011020.09_236	pretraining	MEG
on004078	SMN4Lang: a synchronized multimodal neuroimaging dataset to study brain language processing (Wang et al. 2022)	pretraining	MEG
TUEG	Temple University Hospital EEG Corpus (TUH-EEG), minus the TUAB and TUEV subsets (BioCodec tokenizer only)	pretraining	EEG

• BioCodec single-channel EEG tokenizer (Avramidis et al. 2025), trained on TUH-EEG excluding TUAB and TUEV; frozen inside MEG-XL (encoder + RVQ codebooks kept in this repo)
• MEG-XL masked-token pretraining, ~300 h, 2.5-min windows: Cam-CAN rest + sensorimotor (smt) only, MOUS auditory/listening only, SMN4Lang all MEG; Armeni and Gwilliams (MEG-MASC) appear in the pretraining config only as validation_only sets
• The BioCodec tokenizer used here was trained on TUEG with TUAB and TUEV excluded, as stated in its source above. TUEG is the same collection as TUAB/TUEV and its other sub-corpora, but those two exclusions must not be confused with training on their evaluation recordings. Other sub-corpus overlap requires checking the authors’ preprocessing/split.
• Sources: arXiv:2602.02494 (HTML) Sec. 3 'Pre-training data'; https://github.com/neural-processing-lab/MEG-XL/blob/97da04171d22e8fe4822cfeb3047a57ecf86b318/README.md 'Supported Datasets'; arXiv:2510.09095 (BioCodec, HTML) Sec. 3.1; MEG-XL@97da0417 configs/train_criss_cross_multi_50hz_med.yaml.

Intended input

• MEG only. chs_info must carry sensor positions and coil orientations (loc); magnetometers and gradiometers (planar or axial) get their own type embeddings. The chs_info in config.json (the 306 MEG channels of MNE's test_raw.fif, Neuromag) is only a default.
• 50 Hz, preprocessed as in the authors' code: 0.1-40 Hz band-pass, baseline correction and robust scaling per 3 s chunk, clipped to [-5, 5].
• Pretrained on 150 s windows (n_times=7500); the stored RoPE table covers those 625 token positions, longer windows rebuild it.

Source

• Paper: D. Jayalath, O. Parker Jones. MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training. ICML 2026. arXiv:2602.02494
• Code: neural-processing-lab/MEG-XL @ 97da04171d22e8fe4822cfeb3047a57ecf86b318 (MIT)
• Weights: pnpl/MEG-XL at revision 79838987b0fea6bd55bb9b4cccbe1e6f59e97362, file meg-xl-med-v2.ckpt, sha256 a8be0f06416bebcbea896db8bfdc8027ec25072a64b7e2ab925908dd6edfe88c (model card license: mit).
• The tokenizer inside the checkpoint equals the separate BioCodec release biocodec_ckpt.pt (sha256 b8b5c864bd92242a2aa70c414ab1abe23231772dc2f2e39a8f509fdded728af1, klean2050/BioCodec-release @ 575bf6ea7ae14a34754b217d9b940597a16951bd, MIT), max-abs difference 0.0.

Conversion

convert.py (in this repository) was run with braindecode at bruAristimunha/braindecode@bf95a14d2ea2847013aead661646dcacf51d9aa3 (branch feat/meg-megxl, PR #1554):

python convert.py meg-xl-med-v2.ckpt <out_dir> test_raw.fif

It renames the checkpoint keys to braindecode's and writes config.json with save_pretrained, then model.safetensors / pytorch_model.bin without the head. Tensor values are copied unchanged, except the RoPE table, which the authors' loader recomputes from freqs (torch.polar, 625 positions) and which is stored as (cos, sin) pairs.

Dropped (pretraining only or duplicated): the BioCodec decoder, the duplicated biocodec_embedding_* codebooks, mask_token, the code-prediction output_head, the complex RoPE cache and the Lightning/optimizer state.

file	sha256
model.safetensors	0817547bc254bae86fd74c993a4c5ed941e2e2f35c16d2990046cf32916a99cf
pytorch_model.bin	cb8b1cb6642bd8ee8070d859b3158b979342a9878f18aee2e7e4dde06c15974e
config.json	ba072e1d8ccd8db88ed44160deedd8af1499d9ac6884c392732911512d61482c
convert.py	825bfa078974adb4dc54fc3e5d93f2fbece1f2b0530b9c58cd188e448d8b675b

Verification

MEGXL.from_pretrained on these files (only final_layer.* missing, no unexpected key; loaded tensors equal convert.py applied to the original checkpoint) against the authors' code with the same weights and inputs, CPU float32, eval mode, 20 s: codes identical, features and the gradients of all 119 parameters of one backward pass equal with max-abs difference 0.0, for 306 Neuromag channels (batch 1 and 2) and 157 KIT axial gradiometers. For that comparison the original's attention dropout is set to 0: the released SelfAttention keeps dropout 0.1 active in eval mode, braindecode turns it off.

License

MIT, as the original release: MEG-XL licence (code; the weights' model card on pnpl/MEG-XL says license: mit) and BioCodec licence. Converted and re-hosted with attribution to the authors; for corrections or removal, open an issue at https://github.com/braindecode/braindecode/issues.

Citation

@inproceedings{jayalath2026megxl,
  title     = {{MEG-XL}: Data-Efficient Brain-to-Text via Long-Context Pre-Training},
  author    = {Jayalath, Dulhan and Parker Jones, Oiwi},
  booktitle = {Forty-third International Conference on Machine Learning (ICML)},
  year      = {2026},
  note      = {arXiv:2602.02494},
}

@article{aristimunha2025braindecode,
  title   = {Braindecode: a deep learning library for raw electrophysiological data},
  author  = {Aristimunha, Bruno and others},
  journal = {Zenodo},
  year    = {2025},
  doi     = {10.5281/zenodo.17699192},
}