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Open AccessJournal ArticleDOI

Lucid Dreaming Occurs in Activated REM Sleep, Not a Mixture of Sleep and Wakefulness.

Ben Baird, +2 more
- 15 Feb 2022 - 
- Vol. 45, Iss: 4
TLDR
Lucid dreams are associated with higher-than-average levels of physiological activation during REM sleep, including measures of both subcortical and cortical activation.
Abstract
STUDY OBJECTIVES 1) To replicate the finding that lucid dreams are associated with physiological activation, including heightened REM density, during REM sleep. 2) To critically test whether a previously reported increase in frontolateral 40 Hz power in lucid REM sleep, used to justify the claim that lucid dreaming is a "hybrid state" mixing sleep and wakefulness, is attributable to the saccadic spike potential (SP) artifact as a corollary of heightened REM density. 3) To conduct an exploratory analysis of changes in EEG features during lucid REM sleep. METHODS We analyzed 14 signal-verified lucid dreams (SVLDs) and baseline REM sleep segments from the same REM periods from six participants derived from the Stanford SVLD database. Participants marked lucidity onset with standard left-right-left-right-center (LR2c) eye-movement signals in polysomnography recordings. RESULTS Compared to baseline REM sleep, lucid REM sleep had higher REM density (p=0.002). Bayesian analysis supported the null hypothesis of no differences in frontolateral 40 Hz power after removal of the SP artifact (BH=0.18) and ICA correction (BH=0.01). Compared to the entire REM sleep period, lucid REM sleep showed small reductions in low-frequency and beta band spectral power as well as increased signal complexity (all p<0.05), which were within the normal variance of baseline REM sleep. CONCLUSIONS Lucid dreams are associated with higher-than-average levels of physiological activation during REM sleep, including measures of both subcortical and cortical activation. Increases in 40 Hz power in periorbital channels reflect saccadic and microsaccadic SPs as a result of higher REM density accompanying heightened activation.

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Citations
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Predictive coding, multisensory integration, and attentional control: A multicomponent framework for lucid dreaming

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References
More filters
Journal ArticleDOI

EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.

TL;DR: EELAB as mentioned in this paper is a toolbox and graphic user interface for processing collections of single-trial and/or averaged EEG data of any number of channels, including EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decomposition including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling.
Journal ArticleDOI

FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

TL;DR: FieldTrip is an open source software package that is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data.
Journal ArticleDOI

Nonparametric permutation tests for functional neuroimaging: A primer with examples

TL;DR: The standard nonparametric randomization and permutation testing ideas are developed at an accessible level, using practical examples from functional neuroimaging, and the extensions for multiple comparisons described.
Journal ArticleDOI

Permutation entropy: a natural complexity measure for time series.

TL;DR: The method introduces complexity parameters for time series based on comparison of neighboring values and shows that its complexity behaves similar to Lyapunov exponents, and is particularly useful in the presence of dynamical or observational noise.
Journal ArticleDOI

Removing electroencephalographic artifacts by blind source separation.

TL;DR: The results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods.
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