How well does electrical source imaging reconstruct neural time series? Evidence from simultaneous scalp and intracranial EEG recordings

Electrical source imaging (ESI) estimates neural activity from scalp EEG, but its ability to reconstruct the temporal dynamics of spontaneous brain activity beyond source localization remains unclear. This is assessed using simultaneous high-density EEG and intracranial EEG (iEEG) recordings during spontaneous activity in 34 patients with drug-resistant focal epilepsy. Four ESI approaches (three distributed source models and one beamformer) are applied to estimate neural activity, which is then projected onto iEEG electrode locations to generate virtual iEEG signals for comparison with measured recordings. Reconstruction fidelity is quantified using Pearson correlations between actual and virtual iEEG signals, while spatial accuracy is assessed through peak localization error and spatial deviation derived from point-spread and cross-talk functions. Analyses are performed in broadband and delta, theta, alpha, and beta bands. Temporal reconstruction remains limited, with the highest estimated correlation reaching 0.126, localization errors of approximately 3 cm, and substantial spatial leakage. Reconstruction quality decreases with electrode depth and increasing frequency, while improving with higher epileptic spike rates. No single method is optimal across all metrics, although beamforming shows the weakest temporal reconstruction. These findings indicate that ESI only partially captures spontaneous intracranial dynamics and define spatial and temporal constraints for interpreting EEG-based source reconstructions.

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Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-10-27
DOI
https://doi.org/10.64898/2026.10.26.761837
Primary Topic
EEG and Brain-Computer Interfaces
Type
preprint

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preprint

How well does electrical source imaging reconstruct neural time series? Evidence from simultaneous scalp and intracranial EEG recordings

Nicolas Roehri, Andrea Pigorini, Alessandra Calcagno, Margitta Seeck et al.
3 links
bioRxiv (Cold Spring Harbor Laboratory)
EEG and Brain-Computer Interfaces
preprint

How well does electrical source imaging reconstruct neural time series? Evidence from simultaneous scalp and intracranial EEG recordings

Nicolas Roehri, Andrea Pigorini, Alessandra Calcagno, Margitta Seeck, Serge Vulliémoz, Shahan Momjian, Laurent Spinelli, Ezequiel Mikulan, Steven Beumer, Maria Dimou
preprint en

Abstract

Electrical source imaging (ESI) estimates neural activity from scalp EEG, but its ability to reconstruct the temporal dynamics of spontaneous brain activity beyond source localization remains unclear. This is assessed using simultaneous high-density EEG and intracranial EEG (iEEG) recordings during spontaneous activity in 34 patients with drug-resistant focal epilepsy. Four ESI approaches (three distributed source models and one beamformer) are applied to estimate neural activity, which is then projected onto iEEG electrode locations to generate virtual iEEG signals for comparison with measured recordings. Reconstruction fidelity is quantified using Pearson correlations between actual and virtual iEEG signals, while spatial accuracy is assessed through peak localization error and spatial deviation derived from point-spread and cross-talk functions. Analyses are performed in broadband and delta, theta, alpha, and beta bands. Temporal reconstruction remains limited, with the highest estimated correlation reaching 0.126, localization errors of approximately 3 cm, and substantial spatial leakage. Reconstruction quality decreases with electrode depth and increasing frequency, while improving with higher epileptic spike rates. No single method is optimal across all metrics, although beamforming shows the weakest temporal reconstruction. These findings indicate that ESI only partially captures spontaneous intracranial dynamics and define spatial and temporal constraints for interpreting EEG-based source reconstructions.

bioRxiv (Cold Spring Harbor Laboratory)
University of Geneva (CH), University of Milan (IT), Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (IT), University Hospital of Geneva (CH), Geneva College (US)
European Commission, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Good health and well-being
EEG and Brain-Computer Interfaces
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