专为运动想象脑机接口设计的首个脑电基础模型，提升康复与机器人控制效果。
MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classification
- 基于神经生理学通道模板构建可适配任意电极布局的预处理流程
- 混合预训练策略使新任务仅需每类30次以内试次即达高精度解码
- 适用于卒中康复、辅助机器人等实际场景，性能显著优于现有模型
脑机接口（BCI）实现大脑与外部设备的直接通信。当前脑电（EEG）基础模型虽试图学习跨范式通用表征，但忽略了运动想象（MI）等范式特有的神经生理差异，限制了泛化能力。实际部署中，如卒中康复或辅助机器人应用，研究范式通常在数据采集前已确定。本文提出首个专为运动想象范式设计的脑电基础模型MIRepNet。该模型包含高质量预处理流水线，融合神经生理学指导的电极通道模板，可适配任意头皮电极布局。同时引入混合预训练策略，结合自监督掩码令牌重建与有监督运动想象分类任务，实现仅需少于30次试验/类即可快速适应并准确解码新下游任务。在五个公开的运动想象数据集上的广泛评估显示，MIRepNet持续达到领先性能，显著超越专用与通用脑电模型。代码将开源至GitHub。
原文摘要 · Abstract (English)
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}.
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