Virtual iEEG from Scalp EEG: Charting the Landscape of Source Imaging, Intracranial Inference and Reconstruction

Dongyi He; Xiangkai Wang; Hongjie Yan; Luping Song; Wai Ting Siok; Nizhuan Wang

cs.CVAug 27, 2026

Virtual iEEG from Scalp EEG: Charting the Landscape of Source Imaging, Intracranial Inference and Reconstruction

Authors: Dongyi He, Xiangkai Wang, Hongjie Yan, Luping Song, Wai Ting Siok, Nizhuan Wang

Organizations: Department of Language Science and Technology, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China · School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China · Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China · School of Artificial Intelligence, Southern University of Science and Technology Hospital, Shenzhen, China

Abstract

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.

Figures & tables

Figure 1: Target-centred framework for scalp-to-intracranial EEG inference. (a) Available information comprises scalp EEG $\boldsymbol{x}$ , anatomy $\boldsymbol{A}$ , target-contact geometry $\boldsymbol{r}_{i}$ , deployment context $\boldsymbol{\Omega}$ , and, where permitted, target-patient iEEG adaptation. (b) Prediction targets comprise event inference, feature translation, and waveform reconstruction, with waveform outputs represented as point estimates or conditional distributions. (c) Evidential claims are distinguished by observability, predictability, identifiability, fidelity, and utility.

Dimension	Shared basis	EEG source imaging (ESI)	Scalp-to-intracranial reconstruction (STIR)
Input	Volume-conducted, reference-dependent, sparsely sampled scalp potentials [ 23 ]	Boundary measurements for inverse estimation	Conditioning inputs for virtual-contact estimates or representations
Biophysics	Neural currents; head anatomy; scalp lead field	Explicit forward model; regularised or prior-based inverse [ 23 ]	Paired-data conditional mapping; implicit biophysical relationships; geometric or physical constraints [ 27 , 30 ]
Target	Latent neural information beyond raw scalp traces	Source location; current-density map; source-space time series	iEEG-defined event; time–frequency feature; embedding; contact waveform
Predictive form	Deterministic or probabilistic inference	Regularised or prior-constrained source estimate	Point estimate or conditional distribution
Anatomy	Electrode position; head model; cortical geometry; source depth	Head model; source-space definition	Contact semantics; representation localisation; anatomy-stratified prediction
Data	Anatomical and acquisition metadata	Scalp EEG and head model; optional paired-iEEG validation	Paired or aligned scalp–iEEG; supervised target learning

Table 1: Comparison of EEG source imaging and scalp-to-intracranial reconstruction across targets, modelling assumptions, validation criteria, and evidential limits

Figure 2: Measurement model and identifiability limits in scalp-to-intracranial inference. (a) Scalp EEG $\boldsymbol{x}(t)$ and contact waveform $y_{i}(t)$ are distinct, reference-dependent measurements of partially shared neural activity $\boldsymbol{q}(t)$ . (b) A scalp-null perturbation can alter the intracranial waveform without changing the scalp observation, limiting contact-level identifiability. (b) Reconstruction is therefore formulated as conditional estimation $\boldsymbol{p}(\boldsymbol{y}_{i}\mid\boldsymbol{x},\boldsymbol{A},\boldsymbol{r}_{i},\boldsymbol{\Omega})$ , constrained by anatomy, context, and optional adaptation; priors cannot recover information absent from scalp measurements.

Determinant	Mechanism	Favourable conditions	Limiting conditions
Source extent and synchrony	Net scalp amplitude depends on effective generator extent and field summation; synchrony increases summation but is not a binary prerequisite	Extended recruitment; coherent or otherwise constructively summating generators; high event-to-background SNR	Small effective extent; destructive cancellation; low SNR; empirical area thresholds are not universal [ 51 , 52 , 53 ]
Depth and orientation	Depth, cortical geometry, source orientation, and sensor coverage shape lead-field amplitude and SNR; none alone fixes visibility	Strong scalp projection; prominent deep rhythms under intracranial-informed analysis	Weak projection; low-SNR pure mesial-temporal single events; current subcortical evidence is limited to prominent 8–10-Hz rhythms [ 54 , 55 , 56 ]
Frequency content	Quasi-static propagation is approximately frequency independent; detectability instead depends on generator extent and frequency-specific background SNR	Sufficient extent and SNR; repeated delta signatures or prominent 8–10-Hz target rhythms	Focal, low-amplitude high-frequency generators require favourable SNR; low-frequency evidence does not establish contact-specific HFO recovery [ 52 , 57 , 56 ]
Background state	Neural state changes the recorded activity and can alter scalp–intracranial comparison performance	Pre-specified and matched state; state-stratified evaluation	Pooling wake, NREM sleep, or anaesthesia without stratification [ 50 , 57 ]
Event structure and analysis	Known event timing permits aggregation; averaging tests a repeatable scalp contribution, not single-trial recovery	Repeated, consistently timed events; event-locked or trigger-averaged analysis	Irregularly timed events; low-SNR single-trial targets; extrapolating averaged results to single events [ 55 ]
Reference and montage	Re-referencing is spatial processing and can alter amplitude, polarity, spectral power, and coherence	Explicitly reported scalp and iEEG montages; sensitivity analysis across reasonable references	Unreported or mismatched references; treating montage-specific results as reference-invariant [ 58 , 59 ]

Table 2: Biophysical and empirical determinants of scalp observability, with conditions favouring or limiting intracranial-signal detectability

Figure 3: Generalisation regimes defined by held-out units and target-patient iEEG availability. (a) Held-out unit and prior target-patient iEEG jointly distinguish within-patient, adapted cross-patient, and target-iEEG-free zero-shot evaluation; scalp-only test input does not itself establish zero-shot generalisation. (b) NeuroFlowNet [ 31 ] , CAST [ 32 ] , and Dong et al. [ 33 ] illustrate patient-specific, adapted cross-patient, and scalp-only inference after paired supervision, respectively. (c) Deployment ranges from retrospective settings permitting target-patient iEEG to pre-implant zero-shot use without invasive adaptation; anatomical conditioning cannot restore information absent from scalp measurements.

Study / Model	Cohort and estimand	Principal result	Evidence boundary
Lam et al. [ 25 ]	10 patients contributing 25 scalp-negative mesial-temporal seizures plus 13 patients contributing control records; leave-one-patient-out network detection	Eight of the 25 prespecified seizures plus one previously overlooked seizure detected; three false alarms; 0.31 false alarms/day; PPV 75%	PCA fitted to all seizure and control records before cross-validation; feature set selected on cross-validation performance; 8/9 detections occurred during sleep; local waveform recovery not evaluated
Abou Jaoude et al. [ 41 ]	$n=51$ ; 8,395 h simultaneous scalp–foramen-ovale EEG; patient-stratified fivefold cross-validation; hidden hippocampal-activity detection	ROC AUC $=0.89$ ; precision–recall AUC $=0.39$ ; at PPV $=0.70$ : sensitivity $=0.25$ , false-positive rate $=0.86$ /min	Primary training and expert-labelled testing used sleep; external diagnosis/lateralisation cohorts were scalp-only; awake sensitivity $=0.06$ at the sleep-derived PPV $\simeq 0.9$ threshold using FOnet labels
Antoniades et al. [ 27 ]	Simultaneous scalp–foramen-ovale cohort ( $n=18$ ); 325-ms IED/non-IED trials; leave-subject-out pseudo-iEEG classification	IED accuracy: 62% linear mapping; 65% direct time–frequency scalp features; 68% asymmetric-symmetric-autoencoder representation	The test subject’s scalp data selected the most-correlated of 17 subject-specific mappers; waveform comparison was qualitative; absolute correlation and waveform error were not reported
E2SGAN [ 30 ]	$n=7$ ; simultaneous pre-ictal EEG–SEEG; 15.875-s windows with 75% overlap; leave-one-patient-out evaluation; preselected channel pairs	$\log_{2}$ distance ratios versus raw EEG: DTW $-0.414$ , PSD $-1.480$ , Hellinger $-0.221$ ; best PSD and Hellinger results among compared models	Pair selection used distance intervals and signal similarity; contact-wise correlation and event endpoints were not reported; downsampling to 64 Hz imposed a 32-Hz Nyquist limit; separation relative to overlapping-window generation was not reported
VAE-cGAN [ 43 ]	Reused simultaneous cohort ( $n=18$ ); 320-ms IED/non-IED segments; intra-subject 70:10:20 split and inter-subject ensemble	Intra-subject mean-squared error 0.014, Pearson $r=0.35$ , cosine 0.34; inter-subject IED accuracy 69% versus 68% for ASAE and cGAN	Waveform metrics were intra-subject only; inter-subject models were restricted to training subjects with intra-subject accuracy $>70\%$ ; cross-subject waveform fidelity, diversity, interval coverage, and uncertainty calibration were not evaluated
NeuroFlowNet [ 31 ]	Three selected patients from a nine-patient simultaneous dataset; subject-specific medial-temporal reconstruction; random-trial 90:10 and session-held-out tests	Mean $r$ : random-trial 0.50; session-held-out 0.54; regional session-held-out 0.05–0.84	No cross-patient test; spectral evaluation was mainly 0.5–50 Hz and excluded high-gamma/HFO claims; validation-set coverage of nominal 90% intervals was 38.1–43.2%

Table 3: Representative evidence for scalp-to-intracranial inference, including cohort design, principal results, and boundaries of the supported claims

Electroencephalography

Electroencephalography (EEG) studies brain-generated electrical activity to understand cognition and support diagnosis, monitoring, and brain-computer interfaces.

See all 183 papers

Cross-Subject Intracranial EEG Reconstruction from Scalp Recordings Using Multi-Scale Cross-Attention Transformers
May 17, 2026Tien-Dat Pham, Xuan-The TranCross-Subject EEG Decoding Electroencephalography
HAI-Smartlink Research Lab, Anchi STE Company, Haiphong, Vietnam · School of Mechanical Engineering, Vietnam Maritime University, Haiphong, Vietnam
Prior-Guided Frequency-Calibrated Virtual EEG Channel Inference from Four Frontal Electrodes for Wearable EEG Augmentation
May 28, 2026Minghao XiaoElectroencephalography
School of Biomedical Science and Engineering, South China University of Technology, Guangzhou 511442, China · School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow
Feb 27, 2026Dongyi He, Bin Jiang, Kecheng Feng +5Electroencephalography
School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 400054, China · School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing, 401331, China

May 17, 2026J/K move · Enter open · S save

Cross-Subject Intracranial EEG Reconstruction from Scalp Recordings Using Multi-Scale Cross-Attention Transformers

Tien-Dat Pham, Xuan-The Tran

HAI-Smartlink Research Lab, Anchi STE Company, Haiphong, Vietnam · School of Mechanical Engineering, Vietnam Maritime University, Haiphong, Vietnam

Cross-Subject EEG Decoding Electroencephalography EEG Decoding Transfer Learning

Open paper AskPDF arXiv

Intracranial EEG (iEEG) provides high-fidelity neural recordings essential for clinical and brain-computer interface applications, but acquiring these signals requires invasive surgery. While recent studies have attempted to estimate iEEG from non-invasive scalp EEG, most rely on patient-specific models, creating a circular dependency: if surgery is required to collect training data, the non-invasive model offers limited practical benefit. In this study, we address the challenge of cross-subject iEEG reconstruction by predicting intracranial signals for unseen patients using models trained on other individuals. We propose CAST (Cross-Attention Spatial-Temporal Transformer), a machine learning framework that translates scalp EEG into multi-channel iEEG waveforms through a two-stage transfer learning strategy. First, a temporal encoder extracts multi-scale neural representations at three different resolutions. Then, because electrode placements vary substantially across patients, a channel-aware decoder is calibrated using only a few minutes of data from the target subject. We evaluated the proposed method using leave-one-subject-out cross-validation on two public datasets comprising 1,282 iEEG channels. Experimental results demonstrate that CAST reconstructs cortical signals located near the scalp surface substantially better than deep subcortical activity. In highly observable sensorimotor regions, the model achieved peak correlations of up to r=0.864 in the precentral gyrus. Furthermore, with a channel selection strategy, CAST obtained a mean correlation of r=0.545 on viable subjects, outperforming previous within-subject baselines. These findings indicate that cortical iEEG signals can be reconstructed for unseen subjects from scalp EEG without extensive patient-specific training, and that only a brief calibration phase is sufficient to adapt the model to new hardware configurations.

Abstract

Electroencephalography

Explore similar work

Cross-Subject Intracranial EEG Reconstruction from Scalp Recordings Using Multi-Scale Cross-Attention Transformers

Prior-Guided Frequency-Calibrated Virtual EEG Channel Inference from Four Frontal Electrodes for Wearable EEG Augmentation

Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow