BaRISTA-lobes

BaRISTA, lobes scale

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

Source: pretrained_models/lobes_chans.ckpt at revision 83b27375eba60e9eba9da4e7dd8fb283baace376, sha256 d810338a4929df0fb2421f342b3ee859f9fef269e35fb4f2fd9c55347a63324a. 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 2.3e-06 in float32 on CPU.

Usage

Supply the montage of each batch as spatial_indices, an (n_chans,) tensor of lobes in [0, 21):

import torch
from braindecode.models import BaRISTA

model = BaRISTA.from_pretrained("braindecode/BaRISTA-lobes", n_chans=64, n_outputs=2)
logits = model(torch.randn(8, 64, 6144), spatial_indices=torch.randint(0, 21, (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.

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.