Sep 2026· Journal of NeuroInterventional Surgery· pp. jnis-2026-025329· 0 citations· 28 references
Medicine
TL;DR
This framework enables interpretable and reproducible evaluation of EiEG signal quality and showed high physiological concordance with scalp EEG together with higher spectral fidelity and greater across-bout consistency.
Abstract
Background
Neurointerventional electrophysiology enables minimally invasive access to near-cortical signals without craniotomy. However, a standardized and interpretable signal-quality evaluation framework for electro-intravascular encephalography (EiEG) is lacking, limiting reproducible reporting and principled comparison with conventional scalp electroencephalography (EEG).
Methods
We proposed a framework quantifying physiological concordance, spectral fidelity, and robustness, and validated it in an ovine EiEG model using a stent-mounted electrode deployed in the superior sagittal sinus over the motor cortex. EiEG and spatially corresponding scalp EEG were synchronously recorded across four repeated 5 min bouts using shared hardware and matched preprocessing.
Results
Using the proposed framework in this preparation, we evaluated physiological concordance, spectral fidelity (signal-to-noise ratio (SNR) index), and robustness in simultaneous EiEG and scalp EEG recordings. Concordance was high (Spearman's ρ>0.94). Under the unified SNR definition, the framework quantified overall linear SNR ratios of 2.04 vs Cz and 1.84 vs C1 over 0.5-40 Hz. Across four repeated 5 min recording bouts, EiEG showed greater consistency than scalp EEG, with lower overall-band across-bout coefficient of variation (CV: 0.8% vs 29.8% for Cz and 6.7% for C1) and lower mean absolute relative deviation from bout 1 (1.5% vs 23.6% and 5.4%, respectively). No acute procedure-related complications were observed.
Conclusions
This framework enables interpretable and reproducible evaluation of EiEG signal quality. When applied to this preparation under synchronized recording, EiEG showed high physiological concordance with scalp EEG together with higher spectral fidelity and greater across-bout consistency.
OBJECTIVE
Endovascular EEG (eEEG) has emerged as a brain monitoring technique that offers a balance between signal fidelity and invasiveness. Endovascular electrodes match subdural recordings in bandwidth and signal-to-noise ratio in animal studies, however, their signal properties remain sparsely quantified in humans....
Luis Torres Mailleux, Kota Araki, Modar Hassan et al.· IEEE transactions on bio-med...· 0 citations
Objective. Stent-electrode arrays enable endovascular brain–computer interfaces (BCI) by recording cortical neural activity from within the superior sagittal sinus and have recently been evaluated in an early feasibility clinical trial in the United States (ClinicalTrials.gov: NCT05035823). For a BCI to be viable, the...
N. Chetty, James Bennett, Hunter R. Schone et al.· Journal of Neural Engineerin...· 0 citations
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...
Dong-Yi He, Xiang-Kai Wang, Hong-Jie Yan et al.· 0 citations
Simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have been used across cognitive and physiological states and in diverse clinical populations. However, the interpretation of cross-modal coupling remains difficult because findings from different analytical frameworks, populatio...
Wsu Yu, R. Yuvaraj, S. Perrey et al.· NeuroImage· 0 citations
Brain-computer interfaces (BCIs) bypass peripheral nerves and muscles to establish direct information pathways between the brain and external devices, providing novel approaches for motor function reconstruction, communication assistance, and neurorehabilitation in patients with neurological disorders. However, convent...
Cheng-Shan Zhang· Frontiers in Neuroscience· 0 citations