Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. Recent EEG foundation models aim to learn generalized representations across diverse BCI paradigms. However, these approaches overlook fundamental paradigm-specific neurophysiological distinctions, limiting their generalization ability. Importantly, in practical BCI deployments, the specific paradigm such as motor imagery (MI) for stroke rehabilitation or assistive robotics, is generally determined prior to data acquisition. To address these issues, we propose MIRepNet, the first EEG foundation model explicitly tailored for the MI paradigm. MIRepNet comprises a high-quality EEG preprocessing pipeline incorporating a neurophysiologically-informed channel template, adaptable to EEG headsets with arbitrary electrode configurations. Furthermore, we introduce a hybrid pretraining strategy that combines self-supervised masked token reconstruction and supervised MI classification, facilitating rapid adaptation and accurate decoding on novel downstream MI tasks with fewer than 30 trials per class. Extensive evaluations across five public MI datasets demonstrate that MIRepNet consistently achieves state-of-the-art performance, significantly outperforming both specialized and generalized EEG models.Our code is on : GitHub - staraink/MIRepNet
📥 Download
You can download our model weights using the following code:
from huggingface_hub import hf_hub_download
filepath = hf_hub_download(repo_id="starself/MIRepNet", filename="MIRepNet.pth")
