README.md

metadata

license: apache-2.0
library_name: braindecode
tags:
  - braindecode
  - pytorch
  - safetensors
  - ieeg
  - seeg
  - mapa

mapa-pretrained

Weights of the MAPA encoder mapa_vits384 (d_model 384, 12 blocks, 21,335,424 parameters), for braindecode.models.MAPA, converted from the authors' release. The classification head is not pretrained (seeded random init); fine-tune or linear-probe before use.

from braindecode.models import MAPA
model = MAPA.from_pretrained("braindecode/mapa-pretrained", n_outputs=2, chs_info=raw.info["chs"], regions=regions)

Channel names are read as clinical contact labels ("LA7" is contact 7 of array LA); regions are DKT names from braindecode.models.mapa.MAPA_DKT_REGIONS. The montage in config.json (4 channels, no regions) is only a default. Input is expected at 2048 Hz, or as the session-normalized spectrogram with normalization="session", sfreq=32.

Source and conversion

• Source: bentang18/MAPA at revision 988efbf31a7d1f38533b848c993a719d6f900b1f, file mapa_vits384.pt (sha256 2d236089a2f1a3cc2827e3f150c4a2ba14c51bbfaf0ce0888f84b92a6eb25a7a), Apache-2.0. The authors' NOTICE is copied in this repository.
• convert_mapa_checkpoint.py (in this repository) renames the feed-forward encoder.blocks.{i}.mlp.fc1/fc2 to mlp.0/mlp.3 (braindecode's FeedForwardBlock), keeps every other key, and writes config.json, model.safetensors and pytorch_model.bin with save_pretrained.
• The converted model's outputs equal braindecode's loading of the original file (max-abs difference 0.0).
• Requires a braindecode version newer than 1.8.1.
• The ablation checkpoints (no_region, no_relpos, no_priors) are not re-hosted; they remain at the source repository.

Citation

@misc{tang2026pretraining,
  title         = {Pretraining for Sample-Efficient Neural Interfaces},
  author        = {Ben Tang and Zachary Spalding and Gregory B. Cogan},
  year          = {2026},
  eprint        = {2609.13507},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  url           = {https://arxiv.org/abs/2609.13507},
}

@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},
}

License

Apache-2.0, as the original MAPA release. The checkpoint was pretrained on the Brain Treebank dataset (CC BY 4.0, https://braintreebank.dev/); see NOTICE.