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< E F G H M N P R S T

Resting-State EEG and Hypnosis (2026)

Vilfredo De Pascalis

Department of Psychology, Sapienza University of Rome, Rome, Italy

Correspondence: vilfredo.depascalis@fondazione.uniroma1.it

Keywords: electroencephalography; resting-state EEG; hypnosis; hypnotizability; functional connectivity

Electroencephalography (EEG) records brain electrical activity using electrodes on the scalp. Resting-state EEG is done while a person is awake and at rest, with eyes open or closed, and not performing a specific task. These recordings provide insight into the brain’s intrinsic functional activity and are crucial for studying the neurophysiology of hypnosis. They can also highlight individual differences in hypnotizability or hypnotic susceptibility, i.e., the ability to respond to hypnotic suggestions.

Neurophysiological research on resting-state EEG has addressed two complementary questions. The first concerns hypnotizability as a characteristic individual trait: do highly hypnotizable individuals, in the waking condition, differ in resting-state EEG activity from low hypnotizable individuals? The second concerns hypnosis as a “state;” does hypnotic induction, in the absence of specific suggestions (neutral hypnosis), alter intrinsic brain activity? Although early studies often treated these questions together, subsequent research demonstrated that they require distinct experimental approaches.

Initial investigations sought to identify a specific marker of hypnotizability through electrophysiological measurements, particularly focusing on alpha activity. The hypothesis was that highly hypnotizable individuals would show distinct cortical activation patterns during quiet wakefulness. Some studies reported greater resting alpha activity in highly hypnotizable participants (e.g., London et al., 1968), but the findings proved inconsistent. Attention subsequently shifted to theta activity, reflecting its role in internally directed attention, imagery, and memory. Various investigations reported greater resting frontal theta activity in highly hypnotizable individuals (Galbraith et al., 1970; Graffin et al., 1995; Sabourin et al., 1990). De Pascalis et al. (1998) reported higher frontal theta and lower frontal alpha in highly hypnotizable individuals during waking rest and resting hypnosis, immediately after hypnotic induction. The same study further showed that resting EEG characteristics associated with hypnotizability differed from those associated with a measure of imaginative suggestibility, indicating that these constructs have partly distinct psychophysiological correlates. Williams and Gruzelier (2001) proposed a more differentiated interpretation, suggesting that theta waves are primarily linked to relaxation, while alpha waves are more closely associated with hypnotizability and the overall hypnotic experience. Their findings indicate that no single EEG rhythm can fully define a person’s hypnotizability.

Researchers made a significant conceptual advance by clearly distinguishing hypnotizability from hypnosis. It is assumed that resting EEG recorded before hypnotic induction reflects stable individual characteristics, while resting EEG recorded during neutral hypnosis reflects changes associated with the hypnotic state itself. Studies specifically examining neutral hypnosis indicate that the process of hypnotic induction leads to relatively modest changes in EEG spectral power. However, it does produce noticeable alterations in subjective experience and brain functional organization. Cardeña et al. (2013) demonstrated that neutral hypnosis is accompanied by characteristic changes in awareness and absorption together with altered brain functional connectivity organization. Fingelkurts et al. (2007) similarly reported reproducible changes in local and long-range cortical connectivity during neutral hypnosis, suggesting that hypnotic induction primarily reorganizes communication among distributed brain systems rather than generating a distinctive EEG rhythm.

This distinction between hypnotizability as a trait and hypnosis as a “state” redirected research toward the broader organization of intrinsic brain oscillatory activity. Functional connectivity analyses showed that hypnosis modifies communication among cortical regions even when changes in EEG power are relatively small. Jamieson and Burgess (2014) reported alterations in theta- and beta-band connectivity following hypnotic induction. In contrast, Panda et al. (2023) demonstrated changes in large-scale network topology involving frontal, parietal, and midline regions. Together, these findings support the view that hypnosis is characterized by dynamic reconfiguration of distributed neural networks rather than by changes in the amplitude of any single EEG frequency.

Recent studies have extended this perspective by emphasizing the dynamic properties of intrinsic brain activity. Measures of EEG signal complexity distinguish highly from less hypnotizable individuals more consistently than conventional spectral analyses (Rho et al., 2021). Likewise, Landry et al. (2024a, 2024b) showed that both the aperiodic component of resting EEG and the temporal variability of peak alpha frequency provide more informative markers of hypnotic susceptibility and hypnotic induction than analyses based solely on average oscillatory power. Machine-learning analyses further indicate that combinations of oscillatory, connectivity, and network measures predict hypnotic responsiveness more accurately than any single EEG feature (Farahzadi et al., 2024).

The principal contribution of resting-state EEG research has therefore not been the identification of a unique electrophysiological signature of hypnosis. Instead, it has shown that hypnotizability reflects stable characteristics of intrinsic brain network organization, whereas hypnosis involves a transient reconfiguration of those networks. The evolution of the field—from searching for a single EEG rhythm to characterizing the dynamics of distributed brain systems—has substantially advanced understanding of the neurophysiology of hypnosis and provides a framework for future investigations integrating EEG, computational modeling, and multimodal neuroimaging.

References

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