发表机构
Shaanxi Normal University; Microsoft（陕西师范大学; 微软）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Bio-MF，一种无潜变量的一步MeanFlow框架，实现低延迟高保真EEG到fNIRS跨模态生成，通过直接信号空间预测和多种正则化技术，在保持生成质量的同时实现857倍加速，提升混合MI-BCI解码性能。
AI 中文摘要
结合EEG和fNIRS的混合运动想象脑机接口（MI-BCIs）通过利用互补的电生理和血液动力学信息，可以优于仅使用EEG的系统。当配对的EEG-fNIRS采集不可用或不方便时，为了获得这种混合信息，最近的研究聚焦于EEG到fNIRS的跨模态生成。然而，现有方法仍然存在生成速度慢的问题，且通常需要预训练，限制了它们在实时MI-BCI场景中的应用。尽管一步生成模型为低延迟合成提供了一条有吸引力的途径，但去除迭代细化过程会降低生成保真度并引入非生理伪影。为解决这些问题，本文提出了Bio-MF，一种无潜变量的一步MeanFlow框架，用于EEG条件化的fNIRS生成。Bio-MF执行直接的信号空间x预测，将此信号空间输出转换为MeanFlow速度监督，并通过一次网络评估完成推理。为了在异构传感器布局下保留任务相关的血液动力学结构，Bio-MF集成了时空交互式4D编码、跨模态分类器无关引导和噪声级别门控的FFT正则化。在数据集1上，EEG + 合成fNIRS相对于仅EEG，在HbR和HbO上的ACC分别提高了3.37和4.15个百分点。在数据集2上，在未见过的64通道EEG电极帽下，相应的增益仍保持为2.98和2.50个百分点。在RTX PRO 6000 GPU上，Bio-MF生成一个fNIRS试验耗时7.0毫秒，相对于1000步SCDM延迟实现了857倍的加速。这些结果表明，Bio-MF能够实现快速的EEG到fNIRS合成，同时为下游混合MI解码保留任务相关的生成质量。我们的代码可在以下网址获取：此https URL。
英文摘要
Hybrid motor-imagery brain-computer interfaces (MI-BCIs) combining EEG and fNIRS can outperform EEG-only systems by exploiting complementary electrophysiological and hemodynamic information. To obtain such hybrid information when paired EEG-fNIRS acquisition is unavailable or inconvenient, recent studies have focused on EEG-to-fNIRS cross-modal generation. However, existing methods still suffer from slow generation and often require pretraining, limiting their use in real-time MI-BCI scenarios. Although one-step generative models offer an attractive route to low-latency synthesis, removing the iterative refinement process can reduce generation fidelity and introduce non-physiological artifacts. To address these problems, this paper proposes Bio-MF, a latent-free one-step MeanFlow framework for EEG-conditioned fNIRS generation. Bio-MF performs direct signal-space x-prediction, converts this signal-space output into MeanFlow velocity supervision, and completes inference with one network evaluation. To preserve task-relevant hemodynamic structure under heterogeneous sensor layouts, Bio-MF integrates Spatial-Temporal Interactive 4D Encoding, cross-modal classifier-free guidance, and noise-level-gated FFT regularization. On Dataset 1, EEG + synthetic fNIRS improves ACC over EEG-only by 3.37 and 4.15 percentage points for HbR and HbO, respectively. On Dataset 2, the corresponding gains remain 2.98 and 2.50 percentage points under the unseen 64-channel EEG montage. On an RTX PRO 6000 GPU, Bio-MF generates one fNIRS trial in 7.0 ms, corresponding to an 857x speedup over the 1000-step SCDM latency. These results show that Bio-MF enables fast EEG-to-fNIRS synthesis while preserving task-relevant generation quality for downstream hybrid MI decoding. Our code is available at https://github.com/psychosiwa/Bio-MF.
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