MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classification
This work addresses the need for more effective EEG-based motor imagery classification, particularly for applications like stroke rehabilitation and assistive robotics, though it is incremental as it builds on existing foundation model concepts.
The paper tackled the problem of limited generalization in EEG foundation models for brain-computer interfaces by proposing MIRepNet, a model tailored for motor imagery classification, which achieved state-of-the-art performance across five public datasets with rapid adaptation using fewer than 30 trials per class.
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. This paper proposes MIRepNet, the first EEG foundation model 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 demonstrated that MIRepNet consistently achieved state-of-the-art performance, significantly outperforming both specialized and generalized EEG models. Our code will be available on GitHub\footnote{https://github.com/staraink/MIRepNet}.