用AI把新生儿脑电图转成胎儿脑磁图，填补产前大脑发育研究空白。
Diffusion-based translation between unpaired spontaneous premature neonatal EEG and fetal MEG
- 基于双扩散桥的无配对图像转换方法，提升计算效率与结果质量。
- 时间域误差降低近5%，频域彻底解决模式崩溃，信号保真度接近完美。
- 适用于早产儿与胎儿脑活动对比研究，也适合其他信号跨模态转换。
背景与目的：早产新生儿脑活动传统上通过脑电图（EEG）研究，推动了早期神经发育理解。然而，大脑发育始于胎儿阶段，这一关键窗口仍鲜有研究。唯一可记录宫内神经活动的技术是胎儿脑磁图（fMEG），但存在数据质量差、样本稀缺等问题。本文利用人工智能，将成熟的EEG研究成果迁移至fMEG，以深化对产前脑发育的理解，为潜在病理的早期发现与治疗奠定基础。方法：提出一种基于双扩散桥的无配对扩散翻译方法，引入数值积分优化，显著提升结果质量并降低计算成本。模型在30例高分辨率早产儿EEG与44例fMEG的自发活动爆发数据集上训练。结果：相比生成对抗网络（GANs），本方法在时域均方误差上改善近5%，频域完全消除模式崩溃问题，实现近乎完美的信号保真度。结论：本研究在EEG-fMEG无配对转换任务中达到新基准，为早期脑活动分析开辟道路；所提方法亦可推广至其他无配对信号转换场景。
原文摘要 · Abstract (English)
Background and objective: Brain activity in premature newborns has traditionally been studied using electroencephalography (EEG), leading to substantial advances in our understanding of early neural development. However, since brain development takes root at the fetal stage, a critical window of this process remains largely unknown. The only technique capable of recording neural activity in the intrauterine environment is fetal magnetoencephalography (fMEG), but this approach presents challenges in terms of data quality and scarcity. Using artificial intelligence, the present research aims to transfer the well-established knowledge from EEG studies to fMEG to improve understanding of prenatal brain development, laying the foundations for better detection and treatment of potential pathologies. Methods: We developed an unpaired diffusion translation method based on dual diffusion bridges, which notably includes numerical integration improvements to obtain more qualitative results at a lower computational cost. Models were trained on our unpaired dataset of bursts of spontaneous activity from 30 high-resolution premature newborns EEG recordings and 44 fMEG recordings. Results: We demonstrate that our method achieves significant improvement upon previous results obtained with Generative Adversarial Networks (GANs), by almost 5% on the mean squared error in the time domain, and completely eliminating the mode collapse problem in the frequency domain, thus achieving near-perfect signal fidelity. Conclusion: We set a new state of the art in the EEG-fMEG unpaired translation problem, as our developed tool completely paves the way for early brain activity analysis. Overall, we also believe that our method could be reused for other unpaired signal translation applications.
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