braindecode/DIVER-1-0.1s-tiny

DIVER-1, 0.1 s Tiny (iEEG)

The released encoder of Han et al. (2025), the iEEG encoder DIVER-1-0.1s Tiny (patches of 0.1 s at 500 Hz, pretrained on intracranial recordings), 12.7M parameters, converted to braindecode.models.DIVER1.

Source: weights/ieeg_pretrained_weights.pt of DIVER-Project/DIVER-1, sha256 b812093779fb5cf1b76e18f8307df4136b67b008b6cd94c6be75fa286a87864c (the GitHub LFS object is no longer served; the same file is in the authors' Google Drive folder). Conversion casts the bfloat16 tensors to float32, renames them and drops the pretraining-only mask token and reconstruction heads; convert_diver1_weights.py in this repository reproduces it. Encoder features match the official model to 0.0e+00 in float32 on CPU, for intracranial and scalp channels, with muP attention scaling (mup_attention=True).

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
nm000358	AJILE12: Annotated Joints in Long-term Electrocorticography (Peterson et al., 2022; 12 subjects, DANDI 000055)	pretraining	iEEG
private:DIVER-self-collected-iEEG	DIVER self-collected long-term ECoG/sEEG from 25 drug-resistant epilepsy patients (~7 days each, 2 kHz; IRB-approved, multi-site, not released)	pretraining	iEEG

• iEEG-only corpus (DIVER I): 37 subjects, 5,310 h, 352k channel-hours; self-collected part is 25 patients, 4,028 h (~76% of hours)
• Sources: arXiv:2512.19097v3 Table 5 (Summary of DIVER-1 pretraining datasets); arXiv:2512.19097v3 Table 5; arXiv:2512.19097v3 Appendix B.2; https://github.com/DIVER-Project/DIVER-1 README (25638eb).

Usage

Resample to 500 Hz and pass the electrode positions in chs_info (metres, as in MNE). The channel kinds set the modality: SEEG, ECoG and DBS are intracranial, EEG is scalp.

import torch
from braindecode.models import DIVER1

model = DIVER1.from_pretrained(
    "braindecode/DIVER-1-0.1s-tiny", chs_info=raw.info["chs"], n_times=500, n_outputs=2
)
logits = model(torch.randn(8, len(raw.ch_names), 500))

n_times must be a multiple of 50. The saved geometry is 6 channels and 500 samples; the encoder does not depend on it, so pass your own montage, n_times and n_outputs when loading.

Limitations

These files hold the encoder only; the release has no classification head. Braindecode initializes the head on load, so it needs fine-tuning, as in the paper's protocol (scripts/finetune_neuroprobe.sh). The check above covers float32 CPU encoder features, not downstream accuracy, GPU kernels or mixed precision.

Citation

@article{han2025diver,
    title={DIVER-1: Scaling intracranial EEG foundation models for transferable representations},
    author={Han, Danny Dongyeop and Gwon, Yonghyeon and Lee, Ahhyun Lucy and Lee, Taeyang and Lee, Seong Jin and Choi, Jubin and Lee, Sebin and Bang, Jihyun and Lee, Seungju and Park, David Keetae and Yoo, Shinjae and Chung, Chun Kee and Cha, Jiook},
    journal={arXiv preprint arXiv:2512.19097},
    year={2025}
}

License

The weights are released by the DIVER Project under the MIT licence (LICENSE). Braindecode's code is BSD-3-Clause.

Cite Braindecode

If you use this braindecode port in scientific work, cite the software on Zenodo, doi:10.5281/zenodo.17699192, and the original model and datasets cited above.

@software{braindecode,
  author = {Aristimunha, Bruno and
            Guetschel, Pierre and
            Wimpff, Martin and
            Gemein, Lukas and
            Rommel, Cedric and
            Banville, Hubert and
            Sliwowski, Maciej and
            Wilson, Daniel and
            Brandt, Simon and
            Gnassounou, Théo and
            Paillard, Joseph and
            {Junqueira Lopes}, Bruna and
            Sedlar, Sara and
            Moreau, Thomas and
            Chevallier, Sylvain and
            Gramfort, Alexandre and
            Schirrmeister, Robin Tibor},
  title = {Braindecode: toolbox for decoding raw electrophysiological brain data
           with deep learning models},
  url = {https://github.com/braindecode/braindecode},
  doi = {10.5281/zenodo.17699192},
  publisher = {Zenodo},
  license = {BSD-3-Clause},
}