Lucid Dreams Show Unique Brain Patterns
A study identifies distinct brain activity during lucid dreaming, offering insights into consciousness and therapy.
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A recent study led by researchers at Donders Center for Cognitive Neuroimaging, Radboud University Medical Center, offers insights into the neural mechanisms behind lucid dreaming. By analyzing EEG data, the research reveals distinct brain activity patterns that set lucid dreams apart from both non-lucid dreaming and wakefulness.
The study is published in The Journal of Neuroscience.
Understanding lucid dreaming
Lucid dreaming occurs when individuals become aware that they are dreaming, often during rapid eye movement (REM) sleep. This awareness can allow for the content of dreams to be influenced. These experiences blur the conventional boundaries between wakefulness and sleep, offering a unique lens through which to examine consciousness.
Rapid eye movement (REM)
A stage of sleep characterized by rapid movement of the eyes, heightened brain activity and vivid dreaming. It is during REM sleep that lucid dreaming typically occurs, where the dreamer becomes aware of and can sometimes control their dreams.
Since lucid dreams combine features of both dreaming and conscious thought, they offer a natural window into how the brain supports awareness and self-reflection. Understanding the brain activity that underlies lucid dreaming could reveal how we think about ourselves, form memories and navigate reality – both in dreams and in waking life.
Despite this potential, research in the field has been held back by a number of challenges. Lucid dreams are difficult to induce and study in lab settings, often resulting in small sample sizes. EEG recordings, a key method for studying sleep, are prone to interference from eye movements and other artifacts.
While some studies have indicated alterations in spectral activity, these findings have often lacked replication.
Spectral activity
Spectral activity is the analysis of brainwave frequencies recorded by EEG, showing how different frequency bands (like alpha, beta or gamma waves) fluctuate over time.
In the new study, the team set out to identify specific neural signatures of lucid dreaming by examining these spectral patterns in different sleep states.
Measuring brain activity during lucid dreams
Dr. Çağatay Demirel, a researcher from Donders Center for Cognitive Neuroimaging, Radboud University Medical Center, and colleagues conducted a multi-site investigation to uncover reliable brain activity patterns associated with lucid dreaming by combining data from several laboratories.
The study aimed to identify reliable electrophysiological correlates of lucid dreaming by standardizing and aggregating EEG data across diverse recording environments. The final dataset included 43 sleep recordings from 26 individuals, making it the largest EEG dataset focused on lucid dreaming to date.
The team cleaned the EEG data using three key steps that removed unwanted noise and movement.
- Cleaning the data: They applied a method called the PREP pipeline to remove any noise that wasn’t related to brain activity, such as random eye movements or muscle twitches.
- Removing artifacts: The team used Artifact Subspace Reconstruction (ASR) to address "transient artifacts," which are brief but disruptive signals caused by blinking or movement.
- Reducing noise: They applied Signal-Space Projection (SSP) to minimize contamination from common sources of noise, ensuring the brain's true activity was captured without interference.
Once the data was clean, the team compared brain activity during four key conditions: lucid REM sleep, non-lucid REM sleep (both early and late stages) and relaxed wakefulness. They analyzed EEG data at both the sensor level (directly from the scalp) and the source level, which estimates the specific brain regions generating the signals.
At the sensor level, differences between lucid and non-lucid REM were minimal. However, source-level analyses revealed that lucid dreaming was associated with reduced beta-band (12–30 Hz) power in right parietal regions, including the temporo-parietal junction.
Beta-band (12–30 Hz) power
A measure of brainwave activity in the beta frequency range, often linked to alertness, active thinking and cognitive processing.
Right parietal regions
Areas on the right side of the parietal lobe in the brain, involved in spatial awareness, self-perception and integrating sensory information.
During the eye movement signals used to mark the onset of lucidity, an increase in gamma1-band (30–36 Hz) power was also observed in right temporo-occipital areas.
Functional connectivity analyses showed increased alpha-band (8–12 Hz) coherence between cortical regions during lucid dreaming, relative to non-lucid REM sleep.
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Alpha-band (8–12 Hz) coherence
A measure of how synchronized alpha-frequency brainwaves are between different brain regions, often associated with relaxed wakefulness and coordinated neural communication.
Clinical potential and consciousness research
This study strengthens the view that lucid dreaming is a distinct state of consciousness, characterized by specific patterns of brain activity that differ from both non-lucid REM sleep and wakefulness. Rather than being a form of vivid or self-aware dreaming, the results suggest that lucid dreaming involves measurable changes in how different brain regions communicate and function.
By refining EEG analysis methods, the study sets a new benchmark for research on sleep and consciousness. These tools could now be applied to other altered states, helping to improve the reliability of findings in a field often challenged by noisy data and small sample sizes.
The neural patterns observed in this study have implications beyond dreaming, the authors suggest: “this research opens the door to a deeper understanding of lucid dreaming as an intricate state of consciousness by pointing to the possibility that conscious experience can arise from within sleep itself.”
There are also potential clinical uses. Lucid dreaming may help people with recurrent nightmares or PTSD by offering a way to safely interact with and reshape difficult dream content.
“Lucid dreaming offers a unique avenue for voluntarily interacting with and immersively adjusting internal world models whose dysfunction is often at the root of various mental disorders,” said Demirel.
The team hopes its results “may also facilitate the development of neurofeedback and brain-computer interface technologies aimed at inducing lucid dreaming towards unlocking its full clinical potential.”
“The ability to adjust these dysfunctional models during dreaming presents a potentially useful tool for therapy,” Demirel added.
Reference: Demirel Ç, Gott J, Appel K, et al. Electrophysiological correlates of lucid dreaming: sensor and source level signatures. J Neurosci. 2025:e2237242025. doi: 10.1523/JNEUROSCI.2237-24.2025
