发表机构
The Hong Kong Polytechnic University; Chongqing University of Technology; Affiliated Lianyungang Hospital of Xuzhou Medical University; Southern University of Science and Technology Hospital（香港理工大学; 重庆理工大学; 徐州医科大学附属连云港医院; 南方科技大学医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述提出以目标为中心的虚拟iEEG推断框架，明确其与头皮EEG、EEG源成像的差异，指出当前仅能推断部分颅内信息，需更严格验证与独立数据集推动发展。
AI 中文摘要
颅内脑电图（iEEG）可提供时间精度高、空间特异性强的局灶性及深部脑区神经活动信息，但其侵入性和受限的解剖覆盖范围限制了常规应用。这些局限推动了头皮到颅内的推断，当模型输出携带iEEG定义的事件、特征、表征或接触水平波形语义时，该推断被称为虚拟iEEG。本综述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，同时将可预测性与可观测性、可识别性、保真度及效用分离。证据根据队列独立性、解剖与频谱覆盖范围、训练-测试分离及目标患者适配性进行评估。当前研究支持对选定颅内事件、低频成分及任务相关表征的推断，但无法唯一恢复任意接触水平的活动。更严格的验证需适当对照、源成像基线、不确定性评估及增量效用测试。未来进展依赖独立配对数据集及前瞻性证据，证明虚拟iEEG在头皮EEG和EEG源成像之外具有附加价值。
英文摘要
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.
