braindecode/VEMG2Pose-emg2pose

vemg2pose — emg2pose Regression baseline

Meta's released regression_vemg2pose.ckpt from the emg2pose benchmark, rehosted with braindecode's parameter names:

from braindecode.models import VEMG2Pose

model = VEMG2Pose.from_pretrained("braindecode/VEMG2Pose-emg2pose")

16-channel sEMG at 2 kHz in, 20 joint angles per sample out. The encoder uses valid convolutions and consumes a left context of 1790 samples, so windows must be longer than that; the paper trains on 11,790 (10,000 + 1790).

Configuration

decoder="lstm", parameterization="hybrid". The recurrent decoder emits a position and a velocity per joint, taking the position for the first num_position_steps (500 samples, 250 ms) and integrating the velocity thereafter. Trained without a ground-truth initial pose, so forward(x) starts from zeros.

Provenance

Not retrained — the authors' checkpoint with parameter names rewritten. Loaded into braindecode.models.VEMG2Pose and run against the reference emg2pose.pose_modules implementation on the same input, the outputs are bit-identical (max absolute difference 0.0), with all 68 tensors mapped.

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
nm000281	emg2pose: sEMG (sEMG-RD wristbands, 2 kHz, 16 ch/hand) + motion-capture hand pose, 193 users, 370 h (Salter et al. 2024, NeurIPS D&B)	training	EMG

• experiment regression_vemg2pose (TDS + LSTM, hybrid position/velocity). Supervised on the emg2pose train split (Table 3: 158 users, 23 stages, 250.9 h); val split monitored for checkpointing/early stopping (monitor val_loss); held-out user / stage / user-stage test sets not trained on.
• Sources: arXiv:2412.02725 (HTML) Table 3; https://github.com/facebookresearch/emg2pose README 'Downloading Pre-trained Checkpoints'; https://github.com/facebookresearch/emg2pose config/base.yaml.

License

Weights are Meta's, under CC BY-NC-SA 4.0 (non-commercial, share-alike), carried over unchanged. The UmeTrack hand model behind the labels is CC BY-NC 4.0.

• Salter et al. (2024), emg2pose, NeurIPS Datasets and Benchmarks. arXiv:2412.02725

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},
}