FRIST：基于fMRI表征信息的共享空间训练提升仅用EEG的单指脑机接口解码

中文摘要 AI 辅助

手指级别的运动解码对于自然化脑机接口（BCI）控制至关重要，然而，由于手指表征在感觉运动皮层中空间上接近且受容积传导模糊影响，从头皮脑电图（EEG）中进行单指解码仍然具有挑战性。利用功能性磁共振成像（fMRI）的高空间分辨率，我们提出了fMRI表征信息共享空间训练（FRIST），这是一个两阶段的EEG解码框架，首先从同步EEG-fMRI记录中学习fMRI信息的光谱投影，然后利用fMRI导出的类别几何结构来指导EEG预测的残差细化。FRIST通过共享手指标签在记录之间传递信息，无需配对试验，且在推理时仅使用EEG。我们在12名健全参与者的运动执行（ME）和运动想象（MI）任务中，在模拟在线场景的二分类和三分类时间顺序会话留出解码中进行了评估。使用EEGNet作为EEG特征提取器，与仅使用EEG的EEGNet基线相比，FRIST将二分类ME的组平均准确率从66.93%提高到74.53%，三分类ME从44.83%提高到56.58%，二分类MI从80.78%提高到85.63%，三分类MI从60.93%提高到69.90%。FRIST还显示出在目标参与者自身fMRI数据不可用时，也能改善仅EEG的解码。FRIST还泛化到多种EEG解码骨干网络，在使用EEG Conformer作为EEG特征提取器时，二分类MI达到87.40%，三分类MI达到72.54%。这些发现表明，fMRI为EEG表征学习提供了有用的空间约束。FRIST改善了基于非侵入性EEG的手指级BCI解码，提供了一种将fMRI的空间特异性与EEG的实时适用性相结合的多模态策略。

英文摘要

Finger-level motor decoding is important for naturalistic brain-computer interface (BCI) control, yet individual-finger decoding from scalp electroencephalography (EEG) remains challenging because finger representations are spatially close in the sensorimotor cortex and blurred by volume conduction. Leveraging the high spatial resolution of functional MRI (fMRI), we introduce fMRI Representation-Informed Shared-Space Training (FRIST), a two-stage EEG decoding framework that first learns fMRI-informed spectral projections from simultaneous EEG-fMRI recordings and then uses fMRI-derived class geometry to guide residual refinement of EEG predictions. FRIST transfers information across recordings through shared finger labels without requiring paired trials and uses only EEG at inference. We evaluated 12 able-bodied participants during movement execution (ME) and motor imagery (MI) under two-class and three-class chronological session-held-out decoding simulating the online scenario. Using EEGNet as the EEG feature extractor, FRIST increased group average accuracy from 66.93% to 74.53% for two-class ME, from 44.83% to 56.58% for three-class ME, from 80.78% to 85.63% for two-class MI, and from 60.93% to 69.90% for three-class MI compared with the EEG-only EEGNet baseline. FRIST is also shown to improve EEG-only decoding when the target participant's own fMRI data were unavailable. FRIST also generalized across multiple EEG decoding backbones, reaching 87.40% in two-class MI and 72.54% in three-class MI with EEG Conformer as the EEG feature extractor. These findings indicate that fMRI provide useful spatial constraints for EEG representation learning. FRIST improves noninvasive EEG-based finger-level BCI decoding, offering a multimodal strategy for integrating the spatial specificity of fMRI with real-time applicability of EEG.

发表机构

• Carnegie Mellon University（卡内基梅隆大学）

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