MIRepNet：基於腦電圖的運動想像分類流程與基礎模型

摘要

腦機介面（BCIs）實現了大腦與外部設備的直接通訊。近期的腦電圖（EEG）基礎模型旨在學習跨多種BCI範式的通用表徵。然而，這些方法忽視了範式特定的基本神經生理學差異，限制了其泛化能力。值得注意的是，在實際的BCI部署中，如用於中風康復或輔助機器人的運動想像（MI）等特定範式，通常在數據採集前就已確定。本文提出了MIRepNet，這是首個專為MI範式設計的EEG基礎模型。MIRepNet包含一個高質量的EEG預處理流程，整合了基於神經生理學的通道模板，可適應任意電極配置的EEG頭戴設備。此外，我們引入了一種混合預訓練策略，結合了自監督的掩碼令牌重建和監督的MI分類，促進了在新下游MI任務上的快速適應和精確解碼，每類僅需少於30次試驗。在五個公開的MI數據集上的廣泛評估表明，MIRepNet始終達到最先進的性能，顯著優於專門化和通用化的EEG模型。我們的代碼將在GitHub上提供：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：基於腦電圖的運動想像分類流程與基礎模型

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

摘要

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