braindecode/BaRISTA-coords

BaRISTA, coords scale

The released BaRISTA encoder of Oganesian, Hashemi and Shanechi (2025) for the coords spatial scale, converted to braindecode.models.BaRISTA.

Source: pretrained_models/chans_chans.ckpt at revision 83b27375eba60e9eba9da4e7dd8fb283baace376, sha256 400eeacc0697004cb81c9ecf754859da184ffeea40afc8ee7b5930c3b997e1d0. Conversion renames tensors and fuses the released gated projections into the single projection this port uses; convert_barista_weights.py in this repository reproduces it. Encoder tokens match the released forward equations to 5.6e-06 in float32 on CPU.

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
nm000253	Brain Treebank: large-scale intracranial recordings from naturalistic language stimuli (Wang et al., NeurIPS 2024 D&B; 10 subjects, 26 movie sessions, ~43.5 h sEEG)	pretraining	iEEG

• masked latent token reconstruction, 'coords' spatial scale; 17 of 26 sessions, 2 validation and 7 test sessions held out
• Sources: arXiv:2512.12135 (NeurIPS 2025) Section 4.1; arXiv:2512.12135 Section 4.1; https://github.com/ShanechiLab/BaRISTA/blob/83b27375eba60e9eba9da4e7dd8fb283baace376/README.md.

Usage

Supply the montage of each batch as spatial_indices, an (n_chans, 3) tensor of grid coordinates in [0, 200):

import torch
from braindecode.models import BaRISTA

model = BaRISTA.from_pretrained("braindecode/BaRISTA-coords", n_chans=64, n_outputs=2)
logits = model(torch.randn(8, 64, 6144), spatial_indices=torch.randint(0, 200, (64, 3)))

The saved geometry is 64 channels and 6144 samples at 2048 Hz, the pretraining window. Mean pooling makes the encoder independent of both, so pass your own n_chans, n_times and n_outputs when loading.

Limitations

These files hold the encoder only, as does the release. Braindecode initializes the classification head on load, so it needs fine-tuning, as does the learned read-out of the paper's protocol (pooling="learned"), which was never released. The check above covers float32 CPU encoder tokens, not downstream accuracy, GPU kernels or mixed precision.

Citation

@inproceedings{oganesian2025barista,
    title={BaRISTA: Brain Scale Informed Spatiotemporal Representation of Human Intracranial Neural Activity},
    author={Oganesian, Lucine L. and Hashemi, Saba and Shanechi, Maryam M.},
    booktitle={Advances in Neural Information Processing Systems},
    year={2025}
}

License

Copyright (c) 2025 University of Southern California. Educational, research and non-profit use only; commercial use requires an agreement with the USC Stevens Center for Innovation. See NOTICE.txt and the original licence.

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},
}