From Scalp to Source: Precise Phase Retrieval of Intracerebral Epileptic Sources Based on Surface EEG

Abstract

Accurate phase tracking of deep-brain activity is critical for effective closed-loop and phase-locked neuromodulation therapies. However, direct access to deep neural phase through intracranial recordings remains clinically restrictive due to the invasiveness. Here we validate and clinically benchmark the Gábor-Nelson (GN) dipole estimation method for reconstructing deep-brain oscillatory phase from non-invasive scalp EEG. GN is a geometry-based, imaging-independent approach that offers computationally efficient dipole reconstruction and has rarely been applied to source-level phase estimation in human neuroscience. We compared GN with an established MRI-informed Inverse Solution (IS) method using a three-stage reconstruction pipeline consisting of dipole modeling, dimensionality reduction, and frequency-dependent phase-delay correction. Validation is performed using (i) cadaveric recordings, where known ground-truth seizure waveforms were replayed through implanted deep electrodes, and (ii) simultaneous scalp EEG and SEEG recordings in human patients, where pseudo-ground truth was approximated via the intracranial contacts. GN achieved phase accuracy and signal fidelity comparable to IS across both datasets despite requiring no anatomical imaging. In cadaver recordings, phase-corrected reconstruction correlations exceeded r > 0.91 and Δφ < 9° in mean phase error. In patient SEEG data, GN reached up to r ≈ 0.80 with phase offsets suitable for neuromodulatory timing. GN offers a viable, low-barrier, imaging-independent alternative to traditional inverse modeling for non-invasive seizure phase tracking. This framework opens pathways for scalable, phase-locked and closed-loop stimulation therapies in epilepsy and potentially other network-based brain disorders.