EEG Revisited-Neuronal, Glial, and Network Mechanisms Across Scales

AI Summary

EEG arises from cellular and biophysical processes, with pyramidal neurons dominating dipole generation and excitatory and inhibitory balance shaping recorded signals.
EEG captures dynamics across scales: high frequency oscillations reflect network and neuronal mechanisms, while infra-slow and DC shifts implicate glial and ionic processes.
Multiscale EEG enables biomarkers, closed-loop neuromodulation, and precision medicine, with technological and machine learning advances enhancing mechanistic interpretation.

J Clin Neurophysiol. 2026 Apr 20. doi: 10.1097/WNP.0000000000001259. Online ahead of print.

ABSTRACT

Electroencephalography (EEG) has been providing a window into human brain activity for nearly a century, yet its interpretation often remains empirical rather than mechanistic. This review revisits the cellular and biophysical foundations of EEG through a modern perspective, integrating advances from neurophysiology, computational modeling, and cellular neuroscience. We examine the fundamental physics of EEG generation, from transmembrane currents and dipole formation to the spatial summation. Electroencephalography results from coordinated cellular and network activity, reflecting the integrated contributions of excitatory and inhibitory synaptic events, intrinsic conductances, and tissue geometry, with pyramidal neurons acting as dominant generators, while interneurons play a role in rhythmogenesis and oscillatory coupling. Expanding beyond traditional Berger frequencies, we highlight the significance of high-frequency oscillations and the underlying cellular and network mechanisms that generate the high-frequency component of the EEG signal. Conversely, infra-slow fluctuations and direct current shifts reveal the roles of glial networks, ionic homeostasis, and spreading depolarizations in health and disease. Across these domains, EEG uniquely captures neural activity from submillisecond events to long-term state changes spanning days or years, offering an unparalleled temporal range for studying the human brain. Clinically, this multiscale capacity underpins the emergence of EEG-based biomarkers for epilepsy, psychiatric disorders, and disorders of consciousness, while guiding development of closed-loop neuromodulation and precision medicine strategies. With technological advances and the expanding field of machine learning, the role of EEG will continue to expand as we will gain deeper insights into its underlying mechanisms.

PMID:42359657 | DOI:10.1097/WNP.0000000000001259

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