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braindecode/BaRISTA-parcels
BaRISTA-parcels is a feature extraction model from braindecode. Use it when you need embeddings to search or compare text. It is set up for braindecode. The card lists the license as other.
The released BaRISTA encoder of Oganesian, Hashemi and Shanechi (2025) for the parcels spatial scale, converted to braindecode.models.BaRISTA.
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From the Hugging Face model README
The released BaRISTA encoder of Oganesian, Hashemi and Shanechi
(2025) for the parcels spatial scale,
converted to
braindecode.models.BaRISTA.
Source: pretrained_models/parcels_chans.ckpt at revision
83b27375eba60e9eba9da4e7dd8fb283baace376,
sha256 6c234517d286a8e710b09716dc88c713618670df523cfffb89e4c9073f2657c1. 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 3.6e-06 in float32 on CPU.
Supply the montage of each batch as spatial_indices, an (n_chans,) tensor of Destrieux parcels in [0, 121):
import torch
from braindecode.models import BaRISTA
model = BaRISTA.from_pretrained("braindecode/BaRISTA-parcels", n_chans=64, n_outputs=2)
logits = model(torch.randn(8, 64, 6144), spatial_indices=torch.randint(0, 121, (64,)))
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.
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.
@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}
}
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.