MIRepNet

Braindecode-format re-host of the official MIRepNet checkpoint released by Liu et al. The checkpoint can be loaded directly through Braindecode's standard Hugging Face integration:

from braindecode.models import MIRepNet

model = MIRepNet.from_pretrained("braindecode/mirepnet-pretrained")

# Fine-tuning for another task replaces the released three-class head.
model = MIRepNet.from_pretrained(
    "braindecode/mirepnet-pretrained",
    n_outputs=4,
)

Released configuration

• 45 channels in the order stored in mirepnet_channels.json
• 1,000 samples at 250 Hz
• 256-dimensional embedding
• 6 Transformer blocks with 8 heads
• 3-output supervised pretraining head

The paper's 8--30 Hz filtering, resampling, channel-template preparation, and Euclidean alignment are preprocessing steps and are not performed by the model.

Provenance and conversion

• Official code: https://github.com/staraink/MIRepNet at revision edb80d7605f75ba8b72b417a124cc9db07385f72
• Official checkpoint: https://huggingface.co/starself/MIRepNet at revision 9bac0439c0d3e9ffdb40ca675d61a51b439a446e
• Source file: MIRepNet.pth, SHA-256 432288958007e344a5a84a9ffe9d0e5e5c0cb616aef86c85522375a3f4da9aaf

All 109 downstream tensors were converted. The 34 pretraining-only tensors (mask_token, decoder.*, and the upstream embedding.chan_embed.weight, which is not used by the released forward pass) were intentionally omitted. Against the official implementation, maximum absolute error was 2.38e-7 for pooled features and 1.19e-7 for logits. The conversion is reproducible with convert_mirepnet_checkpoint.py.

The source code and checkpoint are distributed under the MIT License. The original copyright notice is preserved in LICENSE.

Limitations

The official repository does not document the semantic ordering of the three pretraining-head outputs. Replace the head with n_outputs=... and fine-tune it for downstream use unless that label mapping has been independently verified. Dataset licenses are separate from the checkpoint's MIT license.

Citation

@article{LIU2026115966,
  title = {MIRepNet: A pipeline and pre-trained model for EEG-based motor imagery classification},
  journal = {Knowledge-Based Systems},
  volume = {343},
  pages = {115966},
  year = {2026},
  issn = {0950-7051},
  doi = {10.1016/j.knosys.2026.115966},
  url = {https://www.sciencedirect.com/science/article/pii/S0950705126006921},
  author = {Dingkun Liu and Zhu Chen and Jingwei Luo and Shijie Lian and Yuheng Chen and Shaojie Hou and Xiaolian Zhu and Dongrui Wu}
}