用头皮脑电非侵入重建深部颞叶颅内脑电，突破信号生成瓶颈。
Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow
- 基于条件归一化流建模脑电信号复杂分布，显式捕捉随机性。
- 在公开数据集上实现深部颞叶全区域高保真波形与功能连接重建。
- 适合神经科学与临床脑疾病研究者，推动无创脑机分析发展。
尽管从非侵入性头皮脑电图（sEEG）获取深部脑区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）信号仍属未充分探索领域，限制了对深部脑区动态的理解。现有研究多集中于传统信号处理或源定位方法，难以捕捉iEEG的复杂波形与随机特性。本文提出NeuroFlowNet，一种基于条件归一化流（CNF）的跨模态生成框架，首次实现从sEEG信号重建整个深部颞叶区域的iEEG信号。该模型通过可逆变换直接建模复杂的条件概率分布，显式捕捉脑电信号的随机性，从根本上避免了现有生成模型常见的模式坍缩问题。同时，模型融合多尺度结构与自注意力机制，有效捕捉细粒度时间细节与长程依赖关系。在公开同步sEEG-iEEG数据集上的验证表明，NeuroFlowNet在时间波形保真度、频谱特征再现及功能连接恢复方面均表现出色。本研究建立了一种更可靠、可扩展的非侵入性深部脑区动态分析新范式。代码已开源：https://github.com/hdy6438/NeuroFlowNet。
原文摘要 · Abstract (English)
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
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