用头皮脑电推断颅内神经活动，探索虚拟颅内脑电的可行性
Virtual iEEG from Scalp EEG: Charting the Landscape of Source Imaging, Intracranial Inference and Reconstruction
- 按事件、特征、波形三类任务区分虚拟颅内脑电方法
- 可有效推断特定颅内事件与低频成分，但无法还原任意电极信号
- 适合脑科学与临床神经工程研究者关注其边界与验证标准
颅内脑电（iEEG）能精确捕捉深部和局灶脑区的神经活动，但因其侵入性及解剖覆盖有限，难以常规使用。这一限制推动了从头皮脑电推断颅内活动的研究，称为虚拟iEEG——当模型输出具备iEEG事件、特征、表示或电极级波形语义时。本文提出以目标为中心的框架，区分事件推断、特征转换与波形重建，并分离可预测性与可观测性、可辨识性、保真度及实用性。评估依据包括队列独立性、解剖与频谱覆盖范围、训练-测试分离及患者特异性适应。现有研究支持对特定颅内事件、低频成分及任务相关表征的推断，但尚无法唯一恢复任意电极级活动。更强验证需恰当对照、源成像基线、不确定性评估及增量效用测试。未来进展依赖于独立配对数据集及前瞻性证据，证明虚拟iEEG在头皮脑电与脑电源成像之外仍具额外价值。
原文摘要 · Abstract (English)
Intracranial electroencephalography (iEEG) provides temporally precise and spatially specific access to neural activity from focal and deep brain regions, but its invasiveness and restricted anatomical coverage limit routine use. These constraints have motivated scalp-to-intracranial inference, termed virtual iEEG when model outputs carry iEEG-defined event, feature, representation, or contact-level waveform semantics. This review presents a target-centred framework distinguishing event inference, feature translation, and waveform reconstruction, while separating predictability from observability, identifiability, fidelity, and utility. Evidence is evaluated according to cohort independence, anatomical and spectral coverage, train--test separation, and target-patient adaptation. Current studies support inference of selected intracranial events, low-frequency components, and task-related representations, but not unique recovery of arbitrary contact-level activity. Stronger validation requires appropriate controls, source-imaging baselines, uncertainty assessment, and incremental-utility testing. Future progress depends on independent paired datasets and prospective evidence that virtual iEEG adds value beyond scalp EEG and EEG source imaging.
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