Authors: Connor CW et al.
Source: Anesthesia & Analgesia, 142(2):245–248, February 2026.
Summary
This editorial reframes how anesthesiologists should think about intraoperative EEG analysis by arguing that the so-called “background noise” of the EEG contains meaningful physiological signal. Using an analogy of analyzing a modern computer with primitive radio-frequency tools, the author illustrates how scale-free activity—information not confined to discrete, repeating frequencies—can appear indistinguishable from noise when viewed through traditional spectral methods, yet still carry essential information about system function.
In the EEG, this scale-free component manifests as the aperiodic “one-over-f” background, described mathematically by a power-law relationship in which signal power decreases as frequency increases. The slope of this relationship is quantified by the aperiodic exponent β. While classic EEG interpretation has focused on periodic oscillations within delta, theta, alpha, beta, and gamma bands, the aperiodic component has received comparatively little attention despite being a fundamental feature of cortical dynamics.
The editorial discusses emerging analytic methods—such as Variational Mode Decomposition (VMD) and FOOOF (Fitting Oscillations and One-Over-F)—that allow separation of EEG spectra into periodic and aperiodic components. This separation reveals that changes traditionally attributed to oscillatory power may instead reflect shifts in synaptic kinetics and excitation–inhibition balance. Prior work demonstrates that during propofol induction, detrending the EEG for aperiodic activity alters interpretation of loss of consciousness, showing that delta power changes are more tightly time-locked to LOC than alpha or beta changes.
Building on this concept, the author highlights recent work showing that aperiodic dynamics differ between anesthetic agents. Propofol and volatile anesthetics produce distinct aperiodic exponents, despite appearing similar on conventional depth-of-anesthesia monitors. A practical example illustrates how measuring the aperiodic exponent from intraoperative EEG can correctly identify sevoflurane as the anesthetic agent, even when the density spectral array appears ambiguous.
The editorial challenges the current “one-size-fits-all” approach of processed EEG monitors, which treat all anesthetics equivalently and rely on aggregated spectral power. It argues that small differences in EEG dynamics may have meaningful implications, particularly as research increasingly links specific EEG features—such as alpha activity—to outcomes like delirium and postoperative cognitive dysfunction. The author further notes that adjunct agents such as ketamine and dexmedetomidine introduce additional complexity that confounds existing EEG indices.
Ultimately, the piece calls for a conceptual shift: aperiodic EEG dynamics should be viewed as signal, not noise. Incorporating both periodic and aperiodic components may enable more precise differentiation between anesthetic agents, improve mechanistic understanding of anesthetic states, and support development of next-generation EEG monitoring algorithms capable of reflecting true hypnotic and analgesic depth in multimodal anesthesia.
Key Points
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EEG contains meaningful aperiodic (scale-free) activity that is often misclassified as noise
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Separating periodic and aperiodic components fundamentally changes EEG interpretation
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The aperiodic exponent β differs between propofol and volatile anesthetics
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Current depth-of-anesthesia monitors ignore agent-specific EEG dynamics
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Aperiodic analysis may improve anesthetic differentiation and monitoring accuracy
What You Should Know
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Apparent EEG power changes may reflect synaptic kinetics rather than oscillatory activity
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Loss of consciousness may be better understood after removing aperiodic signal components
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Propofol and sevoflurane can be differentiated by their aperiodic EEG signatures
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Multimodal anesthetic regimens challenge existing processed EEG indices
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Future EEG monitoring should integrate both periodic and aperiodic dynamics
Thank you for allowing us to highlight and summarize this thought-provoking editorial from Anesthesia & Analgesia, which reframes EEG “noise” as a clinically meaningful signal in modern anesthetic practice.