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This review critically evaluates the emerging field of virtual iEEG, which aims to infer intracranial neural activity from non-invasive scalp EEG recordings. By establishing a target-centered framework, the authors differentiate between event inference, feature translation, and waveform reconstruction while assessing the predictability and utility of current methodologies. The findings indicate that while certain intracranial events and low-frequency components can be inferred, the unique recovery of arbitrary contact-level activity remains unachieved, highlighting the need for more rigorous validation and independent datasets.
Current methods can infer some intracranial events from scalp EEG, but they fall short of accurately reconstructing specific neural activity patterns.
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.
