MIRepNet：基于脑电图的运动想象分类流程与基础模型

摘要

脑机接口（BCI）实现了大脑与外部设备之间的直接通信。近期的脑电图（EEG）基础模型致力于学习跨多种BCI范式的通用表征。然而，这些方法忽视了范式间基本的神经生理学差异，限制了其泛化能力。值得注意的是，在实际的BCI应用中，如用于中风康复或辅助机器人技术的运动想象（MI）等特定范式，通常在数据采集前就已确定。本文提出了MIRepNet，首个专为MI范式设计的EEG基础模型。MIRepNet包含一个高质量的EEG预处理流程，整合了基于神经生理学的通道模板，可适配任意电极配置的EEG头戴设备。此外，我们引入了一种混合预训练策略，结合了自监督的掩码标记重建与监督的MI分类，使得模型能够在每类少于30次试验的新下游MI任务上快速适应并准确解码。在五个公开MI数据集上的广泛评估表明，MIRepNet持续实现了最先进的性能，显著超越了专门化及通用化的EEG模型。我们的代码将在GitHub上公开：https://github.com/staraink/MIRepNet。

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 GitHubhttps://github.com/staraink/MIRepNet.

MIRepNet：基于脑电图的运动想象分类流程与基础模型

MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classification

摘要

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