Electrophysiological Correlates of Lucid Dreaming: Sensor and Source Level Signatures

Abstract

Lucid dreaming (LD) is a state of conscious awareness of the ongoing oneiric state, predominantly linked to REM sleep. Progress in understanding its neurobiological basis has been hindered by small sample sizes, diverse EEG setups, and artifacts like saccadic eye movements. To address these challenges in characterizing the electrophysiological correlates of LD, we introduced an adaptive multistage preprocessing pipeline, applied to human data (male and female) pooled across laboratories, allowing us to explore sensor- and source-level markers of LD. We observed that, while sensor-level differences between LD and nonlucid REM sleep were minimal, mixed-frequency analysis revealed broad low alpha to gamma power reductions during LD compared with wakefulness. Source-level analyses showed significant beta power (12-30 Hz) reductions in right central and parietal areas, including the temporoparietal junction, during LD. Moreover, functional connectivity in the alpha band (8-12 Hz) increased during LD compared with nonlucid REM sleep. During initial LD eye signaling compared with the baseline, source-level gamma1 power (30-36 Hz) increased in right temporo-occipital regions, including the right precuneus. Finally, functional connectivity analysis revealed increased interhemispheric and inter-regional gamma1 connectivity during LD, reflecting widespread network engagement. These results suggest that distinct source-level power and connectivity patterns characterize the dynamic neural processes underlying LD, including shifts in network communication and regional activation that may underlie the specific changes in perception, memory processing, self-awareness, and cognitive control. Taken together, these findings illuminate the electrophysiological correlates of LD, laying the groundwork for decoding the mechanisms of this intriguing state of consciousness.

Keywords: REM sleep; consciousness; dreams; lucid dream; metacognition; self-awareness.

Figure 1.

Summary of key electrophysiological findings during LD, including increased gamma activity in the precuneus around initial lucidity eye signaling activation, as well as enhanced frontal gamma and posterior alpha connectivity compared with REM sleep.

Figure 2.

A, Time table of conditions over sessions. The top section shows the individual distributions of condition on- and offsets (colored bars) with respect to a standardized timeline, where 0% denotes recording onset and 100% recording offset. Individual recordings are sorted along the y axis with regard to the LD onset, showing how most LD episodes are collected toward the end of a recording session. Also note that waking condition intervals occur early in the timeline as this was the main selection criterion for this condition (see text for details). The bottom section depicts the distribution of each condition segment's center over the standardized timeline using box plots and individual data points. A notable gap between early and late REM states, accounting for ∼50% of total sleep duration, indicates the passage of one or more sleep cycles during both nap and full-night experiments. The LD segment is positioned close to the later REM phase, with selection slightly earlier on average. B, Overview of the EEG preprocessing, postprocessing and analysis protocol employed in this study. Note that this protocol can handle and integrate diverse EEG setups to allow robust neural correlation analyses. C, Illustration of amplitude and waveform characteristics of EEG, EOG, and EMG activities between LD and wakeful states, showcasing representative segments from four participants. LD, lucid dreaming; REM, rapid eye movement sleep; PREP, early-stage EEG processing pipeline; ASR, artifact subspace reconstruction; SSP, signal-space projection; PSD, power source density; topo, topography.

Figure 3.

A, Example comparison of differently regularized Hilbert series of HEOG signals with regard to SP detection sensitivity and specificity. Note that derivative-based transforms are sensitive to non-SP–related activity as shown by the highlighted peaks. B, Thresholding of polynomial regularized Hilbert series of HEOG detects SP events. In this example, a 99.9 percentile was selected. C, Exemplary effects of SP events during LD on ERSP effects at channel F7. Better cleaning of broadband SP artifacts, especially below 48 Hz, is shown with each cumulative preprocessing step. D, Grand-average ERSPs of SP events to demonstrate SP cleaning across cumulative preprocessing steps. The left two columns depict recordings from 64-channel active electrode setups, the right two columns from 128-channel passive electrode setups. The left and right columns of each column pair show data from channels F7 and F8, respectively. E, Top row, Grand-average butterfly plots and GFPs around SPs for each cumulative preprocessing step. Bottom row, Topographies of SP artifacts for each cumulative preprocessing step in the ∼40 Hz band [36–45 Hz (27, 30)]. Left panel, 64-channel active electrode setup. Right panel, 128-channel active electrode setup. A remarkable flattening of SP-related GFPs can be shown in all of these setups. HEOG, horizontal electro-oculogram; SP, saccadic potential; LD, lucid dreaming; ERSP, event-related spectral perturbation; GFP, global field power.

Figure 4.

Sensor-level PSD, complexity, entropy, and topographical power analysis results. A, Statistical comparison of PSDs across conditions for each frequency band. The colored shapes represent smoothed density estimates. Overlaid box plots display the median and interquartile range, with whiskers indicating the data spread. The magenta line connects the mean values across conditions. B1, Grand-average PSDs by across six common channels with shaded areas showing 95% confidence intervals. Error bars show standard error of the mean. B2, Pairwise mixed-frequency power comparisons between LD and E. REM, L. REM, and wake. The top row displays absolute power differences in dB, while the bottom row illustrates relative power changes (%). The shaded regions indicate 95% confidence intervals. A significant power drop (blue contour) is observed in the lucid versus wake comparison, spanning from 8.78 Hz across the entire frequency range. No significant differences were detected between lucid and E. REM or L. REM across any frequency range. C, Statistical comparisons of LZc, entropy measures, and HF across conditions. Significant differences are indicated by black lines for p < 0.01 and a red line for p < 0.05. Density estimates are shown in color to represent the data distribution, complemented by box plots that indicate the median and interquartile range. D, Sensor-level topography maps contrasting tonic power in LD, REM, and waking conditions within each frequency band. Colors show T values representing relative changes in power. White dots point to electrode locations that belong to significant channel clusters (p < 0.02). PSD, power spectral density; LZc, Lempel–Ziv complexity; HF, Higuchi fractals; LD, lucid dreaming; REM, rapid eye movement sleep.

Figure 5.

A, Thresholded surface-based source–level power for each frequency band derived from dSPM and eLORETA and plotted over cortex images. Each surface significance is shown with five different views (coronal, dorsal, left sagittal, right sagittal, and ventral). Power values of statistically significant clusters contrasting LD, REM, and waking for both dSPM and eLORETA methods. All shown clusters were significant with p < 0.025, except clusters marked with *, which were significant with p < 0.05. The label “n.s.” denotes regions where the results were not statistically significant. B, Grand-average contrasted connectivities between L. REM and E.REM, LD and E. REM, LD and L. REM, and LD and wake across frequency ranges, analyzed using dSPM and eLORETA. Increases in PLI connectivity patterns, significant through cluster-permutation tests, are highlighted in black color (dSPM, LD–E. REM alpha and gamma1; eLORETA, LD–E. REM and LD–L. REM alpha), with no significant reductions detected. All statistical tests were conducted with and without additional bilateral significance threshold adjustments, with the symbols “*” and “**” indicating the p threshold (0.025 and 0.05, respectively) for each visualized significance test. Significant connections employing wPLI-debiased measures strongly overlap the PLI findings. LD, lucid dreaming; REM, rapid eye movement sleep; PLI, phase lag index.

Figure 6.

A, ROI and frequency-band–specific GFP fluctuations of EEG data during LD around the time point of LRLR onset. Error shadings represent 95% confidence intervals. GFPs are normalized on the baseline interval which is shown in light red shading 15–10 s before the LRLR onset. Time intervals of significant (p < 0.05) spatiotemporal activation of EEG data are marked in gray shading and with a dash-dot line. White dots on topographies indicate significant (p < 0.05) cluster channels. Inlays show clusters exceeding the cluster threshold at the sensor- and source-level of this time area. B, Source-level activation maps showing significant differences in gamma1 and gamma2 bands, identified using dSPM and eLORETA, relative to the baseline. The label “n.s.” denotes regions where the results were not statistically significant. C, Source-level functional connectivity changes during initial eye signaling compared with the baseline, estimated using dPLI. Connectivity patterns are visualized for both dSPM (left) and eLORETA (right) in gamma1 and gamma2 bands. Circular plots with black lines illustrate significant (p < 0.01) connectivity changes across cortical regions, while heatmaps display T distributions of significant pairwise connections between major brain regions. ROI, region of interest; GFP, global field power; LRLR, left→right→left→right eye signal; dPLI, directed phase lag index.