SCDM：面向MI-BCI中EEG到fNIRS跨模态生成的统一表示学习
SCDM: Unified Representation Learning for EEG-to-fNIRS Cross-Modal Generation in MI-BCIs
AI总结：
针对混合MI-BCI中EEG与fNIRS难以同头皮共置采集的问题，提出时空受控扩散模型SCDM，通过空间跨模态生成和多尺度时间表示模块实现EEG到fNIRS的高保真生成，实验证明合成信号可达到与真实信号相当甚至更优的联合分类性能。
摘要：
混合运动想象脑机接口（MI-BCI）同时整合脑电图（EEG）和功能性近红外光谱（fNIRS）信号，其性能优于仅基于EEG的系统。然而，由于难以在同一头皮表面共置两类传感器，同时记录EEG和fNIRS信号极具挑战性。这一物理限制使高质量混合信号的获取变得复杂，从而限制了混合MI-BCI的广泛应用。为促进混合EEG-fNIRS信号的获取，本研究提出时空受控扩散模型（SCDM），作为从EEG到fNIRS的跨模态生成框架。该模型利用两个核心模块，即空间跨模态生成（SCG）模块和多尺度时间表示（MTR）模块，在统一表示空间中自适应地学习两类信号各自的潜在时间和空间表示。SCG模块进一步利用EEG与fNIRS之间的空间关系，将EEG表示映射到fNIRS表示。实验结果表明，合成fNIRS信号与真实fNIRS信号之间具有高度相似性。EEG与合成fNIRS信号的联合分类性能与EEG结合真实fNIRS信号相当，甚至更优。此外，合成信号表现出与真实信号相似的时空特征，同时保持与EEG信号的空间关系。实验结果表明，SCDM可能代表MI-BCI系统中获取混合EEG-fNIRS信号的一种有前景的范式。
英文摘要：
Hybrid motor imagery brain-computer interfaces (MI-BCIs), which integrate both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals, outperform those based solely on EEG. However, simultaneously recording EEG and fNIRS signals is highly challenging due to the difficulty of colocating both types of sensors on the same scalp surface. This physical constraint complicates the acquisition of high-quality hybrid signals, thereby limiting the widespread application of hybrid MI-BCIs. To facilitate the acquisition of hybrid EEG-fNIRS signals, this study proposes the spatio-temporal controlled diffusion model (SCDM) as a framework for cross-modal generation from EEG to fNIRS. The model utilizes two core modules, the spatial cross-modal generation (SCG) module and the multi-scale temporal representation (MTR) module, which adaptively learn the respective latent temporal and spatial representations of both signals in a unified representation space. The SCG module further maps EEG representations to fNIRS representations by leveraging their spatial relationships. Experimental results show high similarity between synthetic and real fNIRS signals. The joint classification performance of EEG and synthetic fNIRS signals is comparable to or even better than that of EEG with real fNIRS signals. Furthermore, the synthetic signals exhibit similar spatio-temporal features to real signals while preserving spatial relationships with EEG signals. Experimental results suggest that the SCDM may represent a promising paradigm for the acquisition of hybrid EEG-fNIRS signals in MI-BCI systems.
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