基于条件归一化流的从头皮脑电无创重建深颞叶颅内脑电
Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow
AI总结：
提出NeuroFlowNet，一种基于条件归一化流的跨模态生成框架，首次从头皮脑电信号重建整个深颞叶区域的颅内脑电信号，解决了高保真重建的难题。
摘要：
尽管从无创头皮脑电图（sEEG）获取深部脑活动对神经科学和临床诊断至关重要，但直接生成高保真颅内脑电图（iEEG）信号仍是一个基本未探索的领域，限制了对深部脑动力学的理解。当前研究主要集中于传统信号处理或源定位方法，这些方法难以捕捉iEEG的复杂波形和随机特性。为应对这一关键挑战，本文引入NeuroFlowNet，一种新颖的跨模态生成框架，其核心贡献在于首次利用sEEG信号重建整个深颞叶区域的iEEG信号。NeuroFlowNet基于条件归一化流（CNF），通过可逆变换直接建模复杂条件概率分布，从而显式捕捉脑信号的随机性，从根本上避免了现有生成模型中常见的模式崩溃问题。此外，该模型集成了多尺度架构和自注意力机制，以稳健地捕捉细粒度时间细节和长程依赖关系。在公开的同步sEEG-iEEG数据集上的验证结果表明，NeuroFlowNet在时间波形保真度、频谱特征再现和功能连接恢复方面具有有效性。本研究为深部脑动力学的无创分析建立了一种更可靠、可扩展的新范式。该研究的代码可在https://github.com/hdy6438/NeuroFlowNet获取。
英文摘要：
Although obtaining deep brain activity from non-invasive scalp electroencephalography (sEEG) is crucial for neuroscience and clinical diagnosis, directly generating high-fidelity intracranial electroencephalography (iEEG) signals remains a largely unexplored field, limiting our understanding of deep brain dynamics. Current research primarily focuses on traditional signal processing or source localization methods, which struggle to capture the complex waveforms and random characteristics of iEEG. To address this critical challenge, this paper introduces NeuroFlowNet, a novel cross-modal generative framework whose core contribution lies in the first-ever reconstruction of iEEG signals from the entire deep temporal lobe region using sEEG signals. NeuroFlowNet is built on Conditional Normalizing Flow (CNF), which directly models complex conditional probability distributions through reversible transformations, thereby explicitly capturing the randomness of brain signals and fundamentally avoiding the pattern collapse issues common in existing generative models. Additionally, the model integrates a multi-scale architecture and self-attention mechanisms to robustly capture fine-grained temporal details and long-range dependencies. Validation results on a publicly available synchronized sEEG-iEEG dataset demonstrate NeuroFlowNet's effectiveness in terms of temporal waveform fidelity, spectral feature reproduction, and functional connectivity restoration. This study establishes a more reliable and scalable new paradigm for non-invasive analysis of deep brain dynamics. The code of this study is available in https://github.com/hdy6438/NeuroFlowNet
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
