1 Analytical Neurophysiological Lab, Department of Neurology, Duke University, Durham, North Carolina, USA; Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
2 Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada; Multimodal Functional Imaging Lab, Department of Biomedical Engineering, McGill University, Montréal, Québec, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada.
3 Analytical Neurophysiological Lab, Department of Neurology, Duke University, Durham, North Carolina, USA; Multimodal Functional Imaging Lab, Department of Biomedical Engineering, McGill University, Montréal, Québec, Canada; Analytical Neurophysiological Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
4 Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada.
5 Multimodal Functional Imaging Lab, Department of Physics and Concordia School of Health, Concordia University, Montréal, Québec, Canada.
6 Multimodal Imaging and Connectome Analysis (MICA) Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
7 Analytical Neurophysiological Lab, Department of Neurology, Duke University, Durham, North Carolina, USA.
8 Analytical Neurophysiological Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
9 Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
10 Physnum Team, Centre De Recherches Mathématiques, Montréal, Québec, Canada; Electrical Engineering Department, École De Technologie Supérieure, Montréal, Québec H3C 1K3, Canada; Center for Advanced Research in Sleep Medicine, Sacré-Coeur Hospital, Montréal, Québec, Canada.
11 Multimodal Functional Imaging Lab, Department of Biomedical Engineering, McGill University, Montréal, Québec, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada; Physnum Team, Centre De Recherches Mathématiques, Montréal, Québec, Canada; Multimodal Functional Imaging Lab, Department of Physics and Concordia School of Health, Concordia University, Montréal, Québec, Canada.
12 Analytical Neurophysiological Lab, Department of Neurology, Duke University, Durham, North Carolina, USA; Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA; Analytical Neurophysiological Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada. Electronic address: birgit.frauscher@duke.edu.

Cortical oscillations across sleep-wake cycles are essential for coordinating functional brain dynamics. High-density electroencephalography (HDEEG) combined with electrical source imaging (ESI) provides a noninvasive approach to map cortical dynamics; however, its ability to capture spatial ongoing oscillations across different vigilance states remains uncertain. Here, we directly compared HDEEG source imaging by comparing it to a normative intracranial EEG (iEEG) atlas from 110 epilepsy patients with electrodes in healthy brain regions (https://mni-open-ieegatlas.research.mcgill.ca/). Wavelet-based Maximum Entropy on the Mean (wMEM) was applied to localize oscillatory patterns using overnight HDEEG recordings from 35 healthy adults (16 females, mean age 31.1±6.3 years). Virtual iEEG (ViEEG) signals were estimated by applying an iEEG forward model to wMEM sources to examine oscillatory patterns across 5 frequency bands, 38 regions, and 4 vigilance states. We found that HDEEG source imaging exhibited comparable spectral patterns of iEEG in low frequencies but overestimated oscillatory activities at high frequencies. Lateral cortical regions exhibited more accurate source estimation than medial regions (p<0.05). After removing the aperiodic components, the spectral alignment between ViEEG and iEEG significantly improved except for N3 sleep (p<0.05). Oscillatory peak patterns in ViEEG reflect state-dependent dynamics that are broadly consistent with iEEG peaks (p<0.05). HDEEG-derived ViEEG and magnetoencephalography-derived ViEEG approximated iEEG spectral features, showing complementary correspondence. These findings reveal that vigilance states significantly shape cortical oscillations by altering their spectral and spatial profiles. Our results establish high-density EEG as a powerful tool for large-scale, noninvasive investigations of human sleep neurophysiology and brain network dynamics.