EEG Microstates reveal distinct network dynamics in lucid and non-lucid REM sleep

EEG Microstates reveal distinct network dynamics in lucid and non-lucid REM sleep | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results EEG Microstates reveal distinct network dynamics in lucid and non-lucid REM sleep Xinlin Wang , Emma Peters , Johannes Strelen , Nathan Lockhart , Michael Franklin , Stephen LaBerge , Thomas Koenig , Martin Dresler , Daniel Erlacher doi: https://doi.org/10.1101/2025.02.12.637792 Xinlin Wang 1 Institute of Sport Science, University of Bern , Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emma Peters 1 Institute of Sport Science, University of Bern , Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Johannes Strelen 5 University of Mainz , Rhineland Palatinate, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nathan Lockhart 6 No affiliation Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael Franklin 4 Department of Psychology, Stanford University , Stanford, CA 94305-2130, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stephen LaBerge 4 Department of Psychology, Stanford University , Stanford, CA 94305-2130, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thomas Koenig 2 Translational Research Center, University Hospital of Psychiatry , Bern, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martin Dresler 3 Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center , Nijmegen, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel Erlacher 1 Institute of Sport Science, University of Bern , Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: daniel.erlacher{at}unibe.ch Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract During lucid dreaming, the dreamer is aware that they are dreaming. In past years, numerous studies have explored the characteristics of lucid dreams, which link lucid dreams to vivid visual perception, positive emotions, and wake-like metacognition and executive functions. To determine brain network activity associated with the conscious experience during lucid dreaming, we analyzed 39 sleep recordings (non-lucid REM vs. lucid REM) with 32-channel polysomnography from 8 lucid dreamers, using EEG microstate analysis. We found that microstates A and G dominated during lucid REM sleep compared to non-lucid REM sleep, and microstates B, C, and D dominated during non-lucid REM sleep compared to lucid REM sleep. We explored the correlation of our microstate maps with previous findings based on topographical similarities of the microstate maps in our study. This suggests that in our study, microstate A might be associated with emotional processing, microstate B with visual processing, microstate C with salience network activity, microstate D with executive functions, and microstate G with the default mode network. Our results suggest that lucidity during REM sleep is associated with increased self-visualization, metacognition, and executive processing, along with decreased emotional processing and reduced default mode network activity. Additionally, we found the inverse relationship between the presence of microstates/networks associated with regions that serve specific functions and evidence for the function being used. This might indicate the inhibitory function of the EEG microstates during sleep. Our study provides novel insight into the distinct network dynamics in lucid and non-Lucid REM sleep. Introduction During sleep, we perceive a complex dream world with all our senses. These dream experiences integrate bizarre images and themes into a simulated world. They are characterized by rich primary consciousness ( Hobson, 2009 ), and deficits in the secondary consciousness ( Hobson, 2009 ). Dreamers usually lack insight into the fact that they are dreaming and experience limited thought (e.g., rare self-awareness) ( Domhoff, 2023 ; Kahan & LaBerge, 2011 ). Lucid dreaming is an exception in this regard: A lucid dream is a dream during which the dreamer is aware that they are dreaming ( Kahan & LaBerge, 1994 ). During lucid dreaming, dreamers are able to signal their realization of dreaming using specific eye movement patterns, and even completion of a prearranged task ( Dresler et al., 2011 ; Erlacher et al., 2014 ). In the past years, numerous studies have explored the characteristics of lucid dreams with different methods. Studies applying questionnaires or dream reports suggested that lucid dreams might be linked to increased positive emotions ( Schredl, 2024 ; Voss et al., 2013 ), perception hallucinations ( Drinkwater et al., 2020 ), metacognition ( Dresler et al., 2014 ; Drinkwater et al., 2020 ; Stumbrys et al., 2015 ; Voss et al., 2013 ) and highly attentional ability ( Blagrove et al., 2010 ; Stumbrys & Erlacher, 2017 ). By using EEG, researchers indicated that the power in the beta frequency band (13–19 Hz) was increased during lucid REM in both parietal regions ( Holzinger et al., 2006 ) and gamma band (40Hz) in frontal and frontolateral regions ( Voss et al., 2009 ) compared to non-lucid REM sleep. In neuroimaging studies, compared to non-lucid REM sleep, lucid REM sleep showed increased BOLD activity in the right dorsolateral prefrontal cortex and bilateral frontopolar areas ( Dresler et al., 2012a ). The frontopolar cortex gray matter volume was positively associated with experienced dream lucidity and metacognitive monitoring ( Filevich et al., 2015 ). In addition to the localized effects observed in the specific brain regions, a study by Baird et al. demonstrated that individuals reporting frequent lucid dreaming exhibit increased functional connectivity between the anterior prefrontal cortex (aPFC) and temporoparietal association areas ( Baird et al., 2018 ). This pattern of functional connectivity is associated with increased cognitive control and self-awareness. In light of this, it is particularly interesting to investigate whether lucid REM sleep is associated with the activation of specific global brain networks compared to non-lucid REM sleep. EEG microstates have emerged as a promising tool for measuring the spontaneous fluctuations of activity in large-scale brain networks underlying different conscious experiences ( Michel & Koenig, 2018 ). Researchers have attempted to link microstates to specific underlying neural networks to interpret these variations. In a recent meta-analysis, Tarailis et al. suggested that microstates A and B have been associated with sensory processing (microstate A: audition, arousal; microstate B: vision), microstates C and F with personally significant information processing, microstate D with attention-related processing, microstate E with interoceptive and emotional information processing, microstate G with somatosensory network ( Tarailis et al., 2023 ). Several studies have characterized brain microstate changes from wakefulness to sleep ( Bréchet et al., 2020 ; Brodbeck et al., 2012 ; Diezig et al., 2022 ; Wiemers et al., 2023 ). However, specific components of consciousness during REM sleep, such as emotions, visual perception, executive function, and metacognition, have not been clarified. Therefore, this study aims to determine brain network activity associated with the conscious experience during lucid dreaming. Based on the functions of different microstates and the characteristics of lucid dreaming, we have a strong hypothesis that the presence of visual-related microstate B, self-related microstate C, cognitive control microstate D, and interoceptive-related microsite E will have a difference between lucid REM sleep and non-lucid REM sleep. We have a weak hypothesis about microstate A. We did not expect significant changes in microstates F and G. To test these hypotheses, we extracted the EEG data from non-lucid REM sleep and lucid REM sleep and contrasted them within the subject for the microstate analysis. Materials and Methods Participants Nine proficient lucid dreamers (5 men and 4 women) participated in the study (age: 20–48 years). Participants were selected for high self-reported frequency of lucid dreams, with a demonstrated ability to successfully have lucid dreams in a sleep laboratory setting. All participants had no history of neurological disorder and had normal or corrected-to-normal vision. Signed informed consent was obtained from all participants before the experiment, and ethical approval was obtained from the Stanford University Institutional Review Board. The protocol was conducted in accordance with the declaration of Helsinki. Procedure Participants arrived at 9:00 pm. After the EEG setup, participants were briefed on the lucid dreaming task by the experimenter. In addition to having lucid dreams, participants were instructed to perform the different lucid dream tasks (eye smooth tracking or hand clenching) in lucid dreams, as accurately as possible during the lucid dream. They were specifically trained to perform a specific left-right-left-right-center eye movement (LRLR), a standardized method for participants to communicate their lucid dreaming ( La Berge et al., 1981 ). They were instructed to signal the beginning and ending of a lucid dream and the initiation and completion of each task trial during lucid dreaming ( Fig. 1 ). Under the experimental constraints, we obtained as many trials from participants as possible. Download figure Open in new tab Figure 1. A lucid REM sleep dreaming from a single participant. A. Three channels of physiological data (HEOG, VEOG, and EMG) are shown during an initial period of NREM, REM period (90 seconds before lucid dreaming onset), transition to lucid REM sleep, and awakening. Data was bandpass filtered by 0.1 Hz. The dashed line shows a period of lucid dreaming: The participant reported becoming lucid in the dream and making an LRLR signal; when he was ready to wake up, he gave another LRLRLRLR signal. Red squares show the extracted artifact-free EEG data from lucid REM sleep. Yellow squares show the extracted artifact-free EEG data from non-lucid REM sleep. B. Dream report from this participant (see Supplementary Note 1). After participants woke up from a lucid dream, they were instructed to write about their dream experiences in as much detail as possible. Subsequently, they filled out the Subjective Experiences Rating Scale (SERS) ( Kahan, 1994 ), a questionnaire used to assess nine specific: sensory qualities, affective qualities, cognition, recall, sleepiness, general reality orientation, point of view, dreaming, and metacognition. Unfortunately, some of the questionnaires were missing, so we did not do further analysis with these questionnaires. EEG recording Participants spent between 1 and 32 nights in the lab with continuous EEG recording. EEG signals were recorded by Neuroscan 32-channel system (Neuroscan, El Paso, TX, USA), with 29 EEG electrodes placed according to the international 10-20 system (Jasper, 1958), a submental electromyogram (EMG) electrode, and vertical and horizontal electrooculogram (EOG) electrodes. The sampling rate was 250 Hz. All electrodes were applied with Grass EC2 electrode paste and covered by a white net. EEG preprocessing EEG Data Selection The original data set recorded included 105 lucid dream recordings from 9 participants. These lucid dreams were validated through three different criteria ( Erlacher & Stumbrys, 2020 ), and should meet all three criteria: (1) the participant’s subjective self-rating of lucidity; (2) the dream report rated by an external judge as having either possible or clear signs of lucidity; (3) the participant performs LRLR eye signaling, which can be unambiguously identified on the EEG recording by the research team. From the 105 lucid dream recordings, each EEG recording was manually inspected to determine its eligibility for inclusion in the EEG microstate analysis. The inclusion criteria were as follows:1) clear visible eye-signaling (LRLR) at lucidity onset, 2) lucid dream recording segment with a duration longer than 6 seconds, and 3) minimal artifacts, such as muscle activity (myogenic artifacts), eye movements (ocular artifacts), and external interference. Lucid dream segments (lucid REM) containing onsets and offsets marked by LRLR signals were extracted from the EEG data. Any segments with significant ocular and muscular artifacts were manually excluded from each recording night. Then, an equivalent duration of segments (non-lucid REM) was extracted from the 90-second window of REM sleep preceding the lucidity onset from the same recording night ( Fig. 1B ). After the selection, 39 recording nights from 8 participants met the inclusion criteria and were included for further analysis. Lucid-REM sleep (mean length 29.3±21.1 seconds) and non-lucid REM sleep (mean length 29.5±18.1 seconds) segments from each recording night were extracted for EEG microstate analysis (see Supplementary Table 1). EEG Preprocessing EEG data was analyzed using EEGLAB V2023.0 (Delorme & Makeig, 2004) and MATLAB R2022b (Mathworks Inc. Natick, MA, USA). Bad channels were identified by visual inspection and interpolated using spherical splines. EEG data were bandpass filtered between 0.5 Hz and 70 Hz, and notch filtered between 58Hz and 62Hz. Data were re-referenced to the average reference. Independent component analysis (ICA) was computed to remove ocular, muscular, and electrocardiographic artifacts. Finally, the remaining segments presenting any physiological or technical artifacts were removed by visual inspection. Microstate analysis Identifying Individual Microstate Maps The remaining data were filtered between 2 and 20 Hz. EEG microstate analysis was performed using MICROSTATELAB, an EEGLAB toolbox for resting-state microstate analysis ( Nagabhushan Kalburgi et al., 2023 ). First, the Global Field Power (GFP) was computed for each sample over time. Topographies at the local maxima of GFP were clustered for each condition (lucid-REM sleep and non-lucid REM sleep) separately according to a fixed range of cluster numbers (4–7) using a modified k-means algorithm. The polarity of the topographies was ignored. Subsequently, the condition mean microstate maps were identified for the two conditions across individual microstate maps, and the grand mean microstate map was averaged by two condition mean microstate maps. Then, the grand mean microstate map was sorted based on published template maps ( Custo et al., 2017 ), followed by sorting individual and condition mean maps by the grand mean map. Microstate class solution After cluster analysis, we got 4-7 class solutions. To determine the optimal number of microstate classes, we did our analyses following the logic as below: Based on the literature on the function of microstates and the characteristics of lucid dreaming, we had a strong hypothesis that the presence of visual-related microstates B, self-related microstate C, attention-related microstate D, and interoceptive-related microsites E will have a difference between lucid REM sleep and non-lucid REM sleep. We had a weak hypothesis about the emotional-related microstate A. We did not expect changes regarding microstates F and G. Thus, we chose the microstate class number by selecting the smallest set of grand mean microstate maps containing microstate classes A, B, C, D, and E. Microstate Feature Extraction To optimize the comparability of the microstate characteristics extracted ( Nagabhushan Kalburgi et al., 2023 ), all initial individual electric potential field maps were assigned to the grand mean map, which guaranteed a consistent assignment across participants, resulting in a continuous sequence of microstate maps for each individual. Then, features of these identified microstates were extracted. These microstate dynamics features included the average duration in milliseconds (Duration), the average number of occurrences per second (Occurrence), the average coverage of the EEG signal in percent for each microstate class (Coverage), and finally, the explained variance for each microstate class (ExpVar). Statistical analysis In order to assess potential associations between distinct microstate networks and specific aspects of lucid REM and non-lucid REM sleep, we performed separate linear mixed models for dependent variables of duration, occurrence, coverage, ExpVar. The condition (lucid-REM sleep and non-lucid REM sleep) and microstate class were used as fixed factors. A by-subject random intercept was added to represent between-person variability and account for the unbalanced data structure ( Bates et al., 2015 ). We analyzed the data using RStudio 2023.6.1 1 , fitting all models with the package lme4 ( Bates et al., 2015 ). ANOVA tests were performed to test each linear mixed-effects model for significance, and post hoc pairwise comparisons were conducted using the ‘emmeans’ (version 1.8.7) function in the RStudio package with Tukey correction. Type III tests of fixed effects were assessed using Satterthwaite’s method. In addition, to explore whether there are systematic differences in the spatial distribution of one or more microstate classes between different conditions, we compared the topography of the mean maps of the two conditions using topographic analysis of variance (TANOVA) ( Nagabhushan Kalburgi et al., 2023 ). Meta-microstate Analysis In previous studies, assessing microstate map similarity across studies typically relied on visually comparing topographies with previously published figures, which is imprecise and lacks a quantitative description. Also, the inability to search literature based on spatial topographies instead of microstate labels increases the risk of underestimating associations between studies ( Koenig et al., 2023 ). To objectively relate our findings to the existing empirical evidence on EEG resting state microstates, we performed a meta-microstate analysis on our six class microstates using the MATLAB applications: MSTemplateEditor and MSTemplateExplorer ( Koenig et al., 2023 ) ( https://github.com/ThomasKoenigBern/MS-Template-Explorer ). These apps relate microstate findings across studies by comparing the topography similarity, irrespective of the number of classes and labels chosen by the different research groups. We first added the microstate template maps from our data (grand mean microstate maps) using the MSTemplateEditor app. Subsequently, we assessed the topographical similarities between our microstate template maps and those from other studies in the database, generating a similarity matrix with the MSTemplateExplorer app. In our analysis, we selected empirical findings associated with microstate maps from the database that exhibited a topography similarity of over 65% with our template maps and were in some conceptual connection to our hypotheses. A discussion of the association of these empirical findings with our findings was conducted in descending order of topographic similarity. Results Microstate class solution We first identified the microstate class solutions based on our hypothesis. In this study, The 4 class grand mean solution identified microstate classes C, D, E, F, and the 5 class grand mean solution identified microstate classes A, B, C, D, F, did not show all of the maps to test our strong hypotheses. The 6-class solution identified microstate classes A, B, C, D, E, and G, thus containing all classes we were interested in. It was, therefore, considered adequate for our purposes and maintained for further analysis ( Figure 2 ). The 7 class solution (representing microstates A, B, C, D, E, F, G) only added a class (microstate F) that we had no hypothesis about and was therefore not considered further. Download figure Open in new tab Figure 2. The six microstate topographies averaged across participants and conditions TANOVA To confirm the 6-class solution can demonstrate the global network changes in both conditions, we did a TANOVA on the microstate maps. There were no significant differences between microstate class maps of different conditions in the 6-class solution (all p-values above 0.1). This result indicates that generators of the maps of non-lucid REM sleep and lucid REM sleep were similar. Therefore, the parameters of these six microstate maps were considered sufficient to describe global network changes during both non-lucid REM sleep and lucid REM sleep. The resulting grand mean microstate maps of the 6-class solution are shown in Fig. 2 . Modeling of Microstate Parameters Next, we observed differences between conditions in the temporal dynamics of the sixmicrostates (see Figure 3 ). The mixed models computed for the different microstate parameters yielded significant effects, including the factor condition for microstate coverage, duration, occurrence, and explained variance. The findings are visualized in Fig. 3 and briefly reviewed below. Download figure Open in new tab Figure 3. The mean and standard error of coverage (A), occurrence (B), duration (C), and Explained Variance (D) for each microstate class in both conditions. Error bar was calculated by Standard Error. Coverage The results showed a significant interaction effect between microstate classes and condition (non-lucid REM vs. lucid REM) (F (5, 456) = 5.07, p < 0.001). The conditional t-tests showed that coverage of microstate A (t(453) = 2.179, p <0.05) and E (t(453) = 3.031, p <0.01) were significantly higher during lucid REM sleep than non-lucid REM sleep, whereas microstate B (t(453) = 2.179, p <0.05) and D (t(453) = 2.534, p <0.05) were significantly higher during non-lucid REM sleep than lucid REM sleep. These results indicate that, compared to non-lucid REM sleep, the brain spent more time in neural networks represented by microstates A and E during lucid REM sleep and less time in neural networks represented by microstates B and D. Occurrence The results showed a significant interaction effect between microstate class and condition (F (5, 456) = 5.15, p < 0.001). There was no significant effect for the condition ( p =0.056). In the subsequent conditional t-tests, the results showed that the microstate A (t(453) = 2.642, p <0.01) and E (t(453) = 4.133, p <0.0001) had a significantly higher frequency of activating during lucid-REM sleep than during non-lucid REM sleep. Duration The results indicated a significant main effect for condition (F (1, 443.45) = 15.61, p < 0.001) and a significant interaction effect between microstate class and condition (F (5, 443.45) = 2.63, p < 0.05). In the follow-up t-tests, microstates of class B (t(450) = 2.988, p <0.01), C (t(450) = 2.598, p <0.01), and D (t(450) = 3.473, p <0.001) were found to last significantly longer during lucid-REM sleep than during non-lucid REM sleep, indicating that these microstates were more stable during the non-lucid REM sleep. Explained Variance There was a significant interaction effect between microstate class and condition (F (5, 452.72) = 5.17, p < 0.001). No significant main effect for the condition was found ( p =0.86). The t-test results showed that the Explained Variance of microstate B (t(452) = 2.595, p <0.01) and D (t(452) = 2.926, p <0.01) were significantly higher during lucid-REM sleep than during non-lucid REM sleep, whereas the Explained Variance of microstate G (t(452) = 2.865, p <0.01) was lower during lucid-REM sleep than non-lucid REM sleep. Cognitive function of microstates To associate cognitive functions between different microstates more objectively and systematically, we performed a topographic similarity analysis between our six class microstates (See Methods) and a database of published microstate maps. Empirical findings associated with microstate maps were primarily chosen from studies on consciousness-related topics that included a within-subject variation of state ( Table 1 ). View this table: View inline View popup Table 1. Topographical similarities between our microstate template maps and those from the consciousness-related studies in the database. Based on the empirical findings associated with similar microstate maps, we mainly focused on the microstates in which we got significant results. Microstate A. According to the findings associated with the maps selected based on spatial similarity, microstate A might connect with arousal. Studies using source imaging techniques or combined EEG-fMRI reported activity for microstate A in areas partially overlapping with the left middle and superior temporal lobe, primary auditory cortex, and left insular cortex ( Custo et al., 2017 ) (94.6%), as well as the bilateral superior, middle temporal gyri and left middle frontal gyrus ( Britz et al., 2010 ) (84.5%). Microstate B. microstate B was primarily related to visual processing. For example, the source localization study on microstate B with 77.5%) showed very strong activity in visual areas: in the left and right occipital cortices (cuneus), including Brodmann areas 17 and 18 (primary visual cortex) ( Custo et al., 2017 ). Microstate C. Based on previous literature, the function of microstate C seems somewhat ambiguous, showing characteristics of both a task-negative network and a control-related network. For instance, the presence of microstate C was associated with mind-wandering (93.8%) ( Zanesco et al., 2021 ), somatic awareness (91.6%) ( Tarailis et al., 2021 ), and showed overlap with DMN regions (91.7%) ( Custo et al., 2017 ). However, in our study, stronger evidence with higher spatial map similarity supports its association with control-related network functions, suggesting that microstate might be more involved in cognitive processes related to monitoring. For example, Schiller et al. reported that alcohol reduced the presence of microstate C (95.4%), which was associated with a decline in higher-order processing of internal information and cognitive control. ( Schiller et al., 2021 ). Additionally, source localization evidence also showed that microstate C overlapped with the salience network (91.7%) ( Custo et al., 2017 ). Microstate D. From source localization and combined EEG-fMRI studies, microstate D (78.7%, 71.7%) generally overlapped with the frontoparietal network ( Britz et al., 2010 ; Custo et al., 2017 ). This was further supported by a brain stimulation study, which found that applying transcranial magnetic stimulation (TMS) over the right intraparietal sulcus (IPS), a node of the dorsal attention network, could alter the presence of microstate D (87%) ( Croce et al., 2018 ). The presence of similar microstate maps is also associated with high cognitive control (63.4%) and externally orientated processing (93.9%) ( Tarailis et al., 2021 ; Tomescu et al., 2022 ). Microstate G. In our study, microstate G proved to be associated with Default Mode Network (DMN). For example, studies on spontaneous thought during wakefulness showed that the appearance of similar maps was positively correlated with self-related thoughts(91.7%) ( Tomescu et al., 2022 ) and self-comfort (86.2%) ( Tarailis et al., 2021 ). TMS study (91.1%) ( Croce et al., 2018 ) and source localization study (83.7%) ( Custo et al., 2017 ) also identified similar maps associated with the DMN. Discussion The results of our study show that microstates B, C, and D were less present during lucid REM sleep, while microstates A and G dominated during non-lucid REM sleep. Considering the inhibitory function of microstates reported in studies of dreaming and dream-like experiences ( Bréchet et al., 2020 ; Deolindo et al., 2021 ; Diezig et al., 2022 ), along with the characteristics of lucid dreaming mentioned earlier, lucid REM sleep was associated with increased visual processing, metacognition, and volitional control; decreased emotional arousal and DMN activity. The pattern of our results confirmed an inverse relationship between microstates and functional correlates, which suggested that the activity of microstates showed the deactivation of related brain networks during REM sleep. Visual processing during REM sleep Lucid dream is generally more perceptually vivid than the average nonlucid dream ( LaBerge & DeGracia, 2000 ). Besides self-reports, this phenomenon was supported by neuroimaging measuring showing greater activation of visual areas during lucid REM sleep compared to non-lucid REM sleep (Dresler et al., 2012). A recent study also showed that the vividness of the experience can be affected by volition control during lucid dreaming ( Raduga et al., 2020 ). In sum, converging evidence suggests lucid dreaming might have more intense visual activation than non-lucid REM sleep. Our study found that the presence of microstate B, decreased during lucid REM sleep compared to non-lucid REM sleep. Based on the assumption of more active visual processing during lucid dreaming, and in line with the potential inhibitory function of microstates, our results suggest that the reduction of microstate class B is indeed a correlate of intense visual processing during lucid dreaming. This fits the findings from a hypnagogic study: the visual character of dream-like experiences was related to the decreased presence of a microstate associated with higher-order visual areas ( Diezig et al., 2022 ). In sum, our study supports the hypothesis that the decreased presence of microstate B represents a more intense visual perception during lucid REM sleep compared to non-lucid REM. Notably, an intriguing finding is that our microstate B topography had a high similarity with a microstate (83.5%) related to self-reflection in Tarailis et al., (2021) . Similar experiences are typical during lucid dreaming, with the activation of the observing-monitoring-reflecting self ( Kahan & LaBerge, 1994 ). Again, assuming an inhibitory effect of microstates, the decreased presence of microstate B might also suggest that self-reflection is more dominant during lucid REM sleep. However, given the limited evidence regarding the relationship between microstate B and self-reflection in previous studies, we cannot conclusively confirm the self-reflective function of our microstate map. Future studies can clarify this relationship further. Volitional dream control during REM sleep During lucid dreaming, dreamers can control their own behaviors within the dream but also shape the dream itself volitionally ( LaBerge & DeGracia, 2000 ; Stumbrys & Erlacher, 2017 ). Such dream control ranges from simple tasks like performing reality checks or flying to more complex actions, such as executing prearranged tasks like clenching their hands, performing mathematical calculations, or even communicating with researchers during the lucid dream ( Erlacher et al., 2014 ; Konkoly et al., 2021 ; Peters et al., 2024 ). In contrast, non-lucid dreams almost lack this kind of volitional dream control ( Voss et al., 2013 ). Thid can be confirmed by the decreased presence of cognitive control-related microstate D during lucid REM sleep compared to non-lucid REM sleep. Aligning with the inhibitory effect of microstate, this reduction reflects increased cognitive control and frontoparietal activities typical in lucid dreaming. This result is also consistent with previous studies, which found an increased BOLD signal ( Dresler et al., 2012b ) and a greater beta-1 activity ( Holzinger et al., 2006 ) in the parietal regions during lucid dreaming compared to non-lucid dreaming. A recent NREM sleep study found that a similar microstate map (81.5%) was more present during sleep compared to wakefulness, indicating deactivation of frontal brain areas during sleep ( Bréchet et al., 2020 ). These evidence supports that the decreased presence of microstate D represents a heightened dream control during lucid REM sleep compared to non-lucid REM. Considering the limited number of channels in our study, we were unable to precisely estimate the sources contributing to our microstate D. Further research with high-density EEG and source analysis could help clarify the specific neural sources involved in cognitive control during lucid dreaming. Activation of default mode network during REM sleep DMN is the likely neural correlate of dreaming: where externally generated experiences and voluntary control were reduced, internally generated and self-focused experiences became more central, leading to increased DMN activity ( Domhoff & Fox, 2015 ; Fox et al., 2013 ). It generally showed anticorrelations with the ‘task-positive network’( Raichle, 2015 ). Following this logic, compared to non-lucid dreaming, dreamers partly regained sensory awareness and voluntary control during lucid dreaming, which increases the involvement of networks related to external attention and control and reduces the recruitment of the DMN. Our results are consistent with this notion, showing that the presence of microstate G was dominant during lucid REM sleep compared to non-lucid REM sleep. Aligning with the inhibitory effect of microstates, this finding indicated that the DMN was more activated during non-lucid REM sleep compared to lucid REM sleep. Similarly, Diezig et al. found that the decreased presence of similar map (88.4%) correlated with increasing hypnagogic experiences ( Diezig et al., 2022 ). The brain areas underlying this map were shown in right-lateralized activity in the middle and inferior temporal gyrus, which partly overlapped with the subsystem of the DMN reported in Andrews-Hanna et al., 2010 study. Therefore, we suggested that the increased presence of microstate G represents a deactivation of DMN during lucid REM sleep compared to non-lucid REM. Activated metacognition during REM sleep The salience network is associated with performance monitoring ( Oliveira et al., 2007 ) and plays an important role in metacognition ( Ham et al., 2013 ). Recently, Asakage et al. found that the salience network was activated during self-recognition from both first-person and third-person perspectives ( Asakage & Nakano, 2023 ). These functional properties align well with the experience of lucid dreaming: dreamers have metacognitive insight into the fact that they are dreaming and can exert volitional control over their experiences. In contrast, non-lucid dream generally exhibits decreased metacognitive processing, often characterized by deficits in self-monitoring and confusion between internally and externally generated experiences ( Perogamvros et al., 2017 ). Based on these findings, we expected that metacognition would be more dominant during lucid REM sleep. Our result showed a decreased duration of microstate C during lucid REM sleep compared to non-lucid REM sleep. Again, considering the inhibitory effect of the microstate, the salience network might be more recruited during lucid REM sleep. In lucid dreaming, participants partly regain sensory awareness and voluntary control, leading to increased conflicts between self-generated and externally generated experiences. As a result, the salience network might be more recruited to monitor and resolve these conflicts, leading to its prolonged activation. Similarly, a recent study found that increased contribution of microstate C’ (77.45% similarity) was associated with suspension of disbelief in VR context, such as the intention to disregard mismatches between the current sensory experience in the virtual reality and prior expectations ( Denzer et al., 2024 ). Therefore, in our study, the decreased presence of microstate C represents a heightened metacognition during lucid REM sleep compared to non-lucid REM. Emotion during lucid dreaming For emotional arousal, the increased presence of microstate A during lucid REM sleep compared to non-lucid REM sleep might suggest that the emotional-related network is less active during lucid dreaming. The intense emotions often experienced in non-lucid dreams can be explained by the activation of the limbic system ( Braun et al., 1997 ; Maquet et al., 1996 ). During lucid dreaming, the dreamer’s awareness of being in a dream and their ability to monitor the dream state may contribute to modulation or reduction in emotional intensity ( de Macêdo et al., 2019 ; Tzioridou et al., 2022 ). Our findings on reduced emotional arousal provide a preliminary indication of differences in emotional processing between lucid and non-lucid REM sleep. Future studies should investigate these differences further. Summary According to the definition of lucid dreams, dreamers can either actively influence the events of the dream or passively observe the progression of the dream ( Stumbrys et al., 2013 ). In our study, we mixed these two conditions. Participants were instructed to perform eye movements to signal the lucidity onset and offset during lucid REM sleep; in some lucid dreams, participants were capable of using eye movements to initiate and complete a task during lucid REM sleep, while others were not. It is possible that the presence of the related microstate maps still partially reflects task-related execution rather than lucidity itself. Future studies need to explore the neural correlates of active and passive lucid dreams in more detail. Another limitation is the imbalance in the contribution of data from individual participants (Supplementary Table 1). Therefore, findings should be interpreted cautiously and carefully generalized across the entire sample. In conclusion, Our findings demonstrate that EEG microstates can reveal distinct network dynamics in lucid and non-lucid REM sleep, linking specific global brain networks to variations in specific consciousness content. Future research could further investigate how these networks interact during different dream experiences. Acknowledgment This work was supported by the Swiss National Science Foundation (SNSF), no. 10001CM_201074. We would like to express our gratitude to all the lucid dreamers for their time and effort, which made this study possible. We also thank Sarah Diezig for assisting with the data analysis and Keith Garcia for project organization and data collection. Footnotes ↵ 1 Posit team (2023). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/ . References ↵ Andrews-Hanna , J. R. , Reidler , J. S. , Sepulcre , J. , Poulin , R. , & Buckner , R. L . ( 2010 ). Functional-Anatomic Fractionation of the Brain’s Default Network . Neuron , 65 ( 4 ), 550 – 562 . doi: 10.1016/j.neuron.2010.02.005 OpenUrl CrossRef PubMed Web of Science ↵ Asakage , S. , & Nakano , T . ( 2023 ). The salience network is activated during self-recognition from both first-person and third-person perspectives . Human Brain Mapping , 44 ( 2 ), 559 – 570 . doi: 10.1002/hbm.26084 OpenUrl CrossRef PubMed ↵ Baird , B. , Castelnovo , A. , Gosseries , O. , & Tononi , G . ( 2018 ). Frequent lucid dreaming associated with increased functional connectivity between frontopolar cortex and temporoparietal association areas . Scientific Reports , 8 ( 1 ), Article 1 . doi: 10.1038/s41598-018-36190-w OpenUrl CrossRef ↵ Bates , D. , Mächler , M. , Bolker , B. , & Walker , S . ( 2015 ). Fitting Linear Mixed-Effects Models Using lme4 . Journal of Statistical Software , 67 , 1 – 48 . doi: 10.18637/jss.v067.i01 OpenUrl CrossRef PubMed ↵ Blagrove , M. , Bell , E. , & Wilkinson , A . ( 2010 ). Association of lucid dreaming frequency with Stroop task performance . Dreaming , 20 ( 4 ), 280 – 287 . doi: 10.1037/a0020881 OpenUrl CrossRef Web of Science ↵ Braun , A. R. , Balkin , T. J. , Wesenten , N. J. , Carson , R. E. , Varga , M. , Baldwin , P. , Selbie , S. , Belenky , G. , & Herscovitch , P . ( 1997 ). Regional cerebral blood flow throughout the sleep-wake cycle. An H2(15)O PET study . Brain , 120 ( 7 ), 1173 – 1197 . doi: 10.1093/brain/120.7.1173 OpenUrl CrossRef PubMed Web of Science ↵ Bréchet , L. , Brunet , D. , Perogamvros , L. , Tononi , G. , & Michel , C. M . ( 2020 ). EEG microstates of dreams . Scientific Reports , 10 ( 1 ), Article 1 . doi: 10.1038/s41598-020-74075-z OpenUrl CrossRef ↵ Britz , J. , Van De Ville , D. , & Michel , C. M. ( 2010 ). BOLD correlates of EEG topography reveal rapid resting-state network dynamics . NeuroImage , 52 ( 4 ), 1162 – 1170 . doi: 10.1016/j.neuroimage.2010.02.052 OpenUrl CrossRef PubMed Web of Science ↵ Brodbeck , V. , Kuhn , A. , von Wegner , F. , Morzelewski , A. , Tagliazucchi , E. , Borisov , S. , Michel , C. M. , & Laufs , H. ( 2012 ). EEG microstates of wakefulness and NREM sleep . NeuroImage , 62 ( 3 ), 2129 – 2139 . doi: 10.1016/j.neuroimage.2012.05.060 OpenUrl CrossRef PubMed ↵ Croce , P. , Zappasodi , F. , & Capotosto , P . ( 2018 ). Offline stimulation of human parietal cortex differently affects resting EEG microstates . Scientific Reports , 8 ( 1 ), 1287 . doi: 10.1038/s41598-018-19698-z OpenUrl CrossRef PubMed ↵ Custo , A. , Van De Ville , D. , Wells , W. M. , Tomescu , M. I. , Brunet , D. , & Michel , C. M. ( 2017 ). Electroencephalographic Resting-State Networks: Source Localization of Microstates . Brain Connectivity , 7 ( 10 ), 671 – 682 . doi: 10.1089/brain.2016.0476 OpenUrl CrossRef PubMed ↵ de Macêdo , T. C. F. , Ferreira , G. H. , de Almondes , K. M. , Kirov , R. , & Mota-Rolim , S. A. ( 2019 ). My Dream, My Rules: Can Lucid Dreaming Treat Nightmares? Frontiers in Psychology , 10 . doi: 10.3389/fpsyg.2019.02618 OpenUrl CrossRef ↵ Denzer , S. , Diezig , S. , Achermann , P. , Mast , F. W. , & Koenig , T . ( 2024 ). Electrophysiological (EEG) microstates during dream-like bizarre experiences in a naturalistic scenario using immersive virtual reality . European Journal of Neuroscience , 60 ( 8 ), 5815 – 5830 . doi: 10.1111/ejn.16530 OpenUrl CrossRef PubMed ↵ Deolindo , C. S. , Ribeiro , M. W. , de Aratanha , M. A. A. , Scarpari , J. R. S. , Forster , C. H. Q. , da Silva , R. G. A. , Machado , B. S. , Amaro Junior , E. , König , T. , & Kozasa , E. H. ( 2021 ). Microstates in complex and dynamical environments: Unraveling situational awareness in critical helicopter landing maneuvers . Human Brain Mapping , 42 ( 10 ), 3168 – 3181 . doi: 10.1002/hbm.25426 OpenUrl CrossRef PubMed ↵ Diezig , S. , Denzer , S. , Achermann , P. , Mast , F. W. , & Koenig , T . ( 2022 ). EEG Microstate Dynamics Associated with Dream-Like Experiences During the Transition to Sleep . Brain Topography . doi: 10.1007/s10548-022-00923-y OpenUrl CrossRef ↵ Domhoff , G. W . ( 2023 ). The relationship between dreaming and autonoetic consciousness: The neurocognitive theory of dreaming gains in explanatory power by drawing upon the multistate hierarchical model of consciousness . Dreaming , 33 ( 1 ), 1 – 18 . doi: 10.1037/drm0000233 OpenUrl CrossRef ↵ Domhoff , G. W. , & Fox , K. C. R . ( 2015 ). Dreaming and the default network: A review, synthesis, and counterintuitive research proposal . Consciousness and Cognition , 33 , 342 – 353 . doi: 10.1016/j.concog.2015.01.019 OpenUrl CrossRef PubMed ↵ Dresler , M. , Eibl , L. , Fischer , C. F. , Wehrle , R. , Spoormaker , V. I. , Steiger , A. , Czisch , M. , & Pawlowski , M . ( 2014 ). Volitional components of consciousness vary across wakefulness, dreaming and lucid dreaming . Frontiers in Psychology , 4 . doi: 10.3389/fpsyg.2013.00987 OpenUrl CrossRef PubMed ↵ Dresler , M. , Koch , S. P. , Wehrle , R. , Spoormaker , V. I. , Holsboer , F. , Steiger , A. , Sämann , P. G. , Obrig , H. , & Czisch , M . ( 2011 ). Dreamed Movement Elicits Activation in the Sensorimotor Cortex . Current Biology , 21 ( 21 ), 1833 – 1837 . doi: 10.1016/j.cub.2011.09.029 OpenUrl CrossRef PubMed ↵ Dresler , M. , Wehrle , R. , Spoormaker , V. I. , Koch , S. P. , Holsboer , F. , Steiger , A. , Obrig , H. , Sämann , P. G. , & Czisch , M . ( 2012a ). Neural correlates of dream lucidity obtained from contrasting lucid versus non-lucid REM sleep: A combined EEG/fMRI case study . Sleep , 35 ( 7 ), 1017 – 1020 . PubMed. doi: 10.5665/sleep.1974 OpenUrl CrossRef PubMed Web of Science ↵ Dresler , M. , Wehrle , R. , Spoormaker , V. I. , Koch , S. P. , Holsboer , F. , Steiger , A. , Obrig , H. , Sämann , P. G. , & Czisch , M . ( 2012b ). Neural Correlates of Dream Lucidity Obtained from Contrasting Lucid versus Non-Lucid REM Sleep: A Combined EEG/fMRI Case Study . Sleep , 35 ( 7 ), 1017 – 1020 . doi: 10.5665/sleep.1974 OpenUrl CrossRef PubMed Web of Science ↵ Drinkwater , K. G. , Denovan , A. , & Dagnall , N . ( 2020 ). Lucid Dreaming, Nightmares, and Sleep Paralysis: Associations With Reality Testing Deficits and Paranormal Experience/Belief . Frontiers in Psychology , 11 . doi: 10.3389/fpsyg.2020.00471 OpenUrl CrossRef ↵ Erlacher , D. , Schädlich , M. , Stumbrys , T. , & Schredl , M . ( 2014 ). Time for actions in lucid dreams: Effects of task modality, length, and complexity . Frontiers in Psychology , 4 . doi: 10.3389/fpsyg.2013.01013 OpenUrl CrossRef ↵ Erlacher , D. , & Stumbrys , T . ( 2020 ). Wake Up, Work on Dreams, Back to Bed and Lucid Dream: A Sleep Laboratory Study . Frontiers in Psychology , 11 . https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01383 ↵ Filevich , E. , Dresler , M. , Brick , T. R. , & Kühn , S . ( 2015 ). Metacognitive Mechanisms Underlying Lucid Dreaming . The Journal of Neuroscience , 35 ( 3 ), 1082 – 1088 . doi: 10.1523/JNEUROSCI.3342-14.2015 OpenUrl Abstract / FREE Full Text ↵ Fox , K. , Nijeboer , S. , Solomonova , E. , Domhoff , G. W. , & Christoff , K . ( 2013 ). Dreaming as mind wandering: Evidence from functional neuroimaging and first-person content reports . Frontiers in Human Neuroscience , 7 . https://www.frontiersin.org/articles/10.3389/fnhum.2013.00412 ↵ Ham , T. E. , Bonnelle , V. , Hellyer , P. , Jilka , S. , Robertson , I. H. , Leech , R. , & Sharp , D. J . ( 2013 ). The neural basis of impaired self-awareness after traumatic brain injury . Brain , 137 ( 2 ), 586 . doi: 10.1093/brain/awt350 OpenUrl CrossRef PubMed ↵ Hobson , J. A . ( 2009 ). REM sleep and dreaming: Towards a theory of protoconsciousness . Nature Reviews Neuroscience , 10 ( 11 ), 803 – 813 . doi: 10.1038/nrn2716 OpenUrl CrossRef PubMed Web of Science ↵ Holzinger , B. , LaBerge , S. , & Levitan , L . ( 2006 ). Psychological correlates of lucid dreaming . Dreaming , 16 . doi: 10.1037/1053-0797.16.2.88 OpenUrl CrossRef ↵ Kahan , T. L . ( 1994 ). Measuring dream self-reflectiveness: A comparison of two approaches . Dreaming , 4 ( 3 ), 177 – 193 . doi: 10.1037/h0094411 OpenUrl CrossRef Kahan , T. L. , & Claudatos , S . ( 2016 ). Phenomenological features of dreams: Results from dream log studies using the Subjective Experiences Rating Scale (SERS) . Consciousness and Cognition , 41 , 159 – 176 . doi: 10.1016/j.concog.2016.02.007 OpenUrl CrossRef PubMed ↵ Kahan , T. L. , & LaBerge , S . ( 1994 ). Lucid Dreaming as Metacognition: Implications for Cognitive Science . Consciousness and Cognition , 3 ( 2 ), 246 – 264 . doi: 10.1006/ccog.1994.1014 OpenUrl CrossRef ↵ Kahan , T. L. , & LaBerge , S. P . ( 2011 ). Dreaming and waking: Similarities and differences revisited . Consciousness and Cognition , 20 ( 3 ), 494 – 514 . doi: 10.1016/j.concog.2010.09.002 OpenUrl CrossRef PubMed ↵ Koenig , T. , Diezig , S. , Kalburgi , S. N. , Antonova , E. , Artoni , F. , Brechet , L. , Britz , J. , Croce , P. , Custo , A. , Damborská , A. , Deolindo , C. , Heinrichs , M. , Kleinert , T. , Liang , Z. , Murphy , M. M. , Nash , K. , Nehaniv , C. , Schiller , B. , Smailovic , U. , … Michel , C. M . ( 2023 ). EEG-Meta-Microstates: Towards a More Objective Use of Resting-State EEG Microstate Findings Across Studies . Brain Topography . doi: 10.1007/s10548-023-00993-6 OpenUrl CrossRef PubMed ↵ Konkoly , K. R. , Appel , K. , Chabani , E. , Mangiaruga , A. , Gott , J. , Mallett , R. , Caughran , B. , Witkowski , S. , Whitmore , N. W. , Mazurek , C. Y. , Berent , J. B. , Weber , F. D. , Türker , B. , Leu-Semenescu , S. , Maranci , J.-B. , Pipa , G. , Arnulf , I. , Oudiette , D. , Dresler , M. , & Paller , K. A . ( 2021 ). Real-time dialogue between experimenters and dreamers during REM sleep . Current Biology , 31 ( 7 ), 1417 – 1427 .e6. doi: 10.1016/j.cub.2021.01.026 OpenUrl CrossRef PubMed ↵ La Berge , S. P. , Nagel , L. E. , Dement , W. C. , & Zarcone , V. P. ( 1981 ). Lucid Dreaming Verified by Volitional Communication during Rem Sleep . Perceptual and Motor Skills , 52 ( 3 ), 727 – 732 . doi: 10.2466/pms.1981.52.3.727 OpenUrl CrossRef PubMed ↵ LaBerge , S. , & DeGracia , D. J. ( 2000 ). Varieties of lucid dreaming experience . In Individual differences in conscious experience (pp. 269 – 307 ). John Benjamins Publishing Company . doi: 10.1075/aicr.20.14lab OpenUrl CrossRef ↵ Maquet , P. , Péters , J.-M. , Aerts , J. , Delfiore , G. , Degueldre , C. , Luxen , A. , & Franck , G . ( 1996 ). Functional neuroanatomy of human rapid-eye-movement sleep and dreaming . Nature , 383 ( 6596 ), 163 – 166 . doi: 10.1038/383163a0 OpenUrl CrossRef PubMed Web of Science ↵ Michel , C. M. , & Koenig , T . ( 2018 ). EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review . NeuroImage , 180 , 577 – 593 . doi: 10.1016/j.neuroimage.2017.11.062 OpenUrl CrossRef PubMed ↵ Nagabhushan Kalburgi , S. , Kleinert , T. , Aryan , D. , Nash , K. , Schiller , B. , & Koenig , T. ( 2023 ). MICROSTATELAB: The EEGLAB Toolbox for Resting-State Microstate Analysis . Brain Topography . doi: 10.1007/s10548-023-01003-5 OpenUrl CrossRef ↵ Oliveira , F. T. P. , McDonald , J. J. , & Goodman , D . ( 2007 ). Performance Monitoring in the Anterior Cingulate is Not All Error Related: Expectancy Deviation and the Representation of Action-Outcome Associations . Journal of Cognitive Neuroscience , 19 ( 12 ), 1994 – 2004 . doi: 10.1162/jocn.2007.19.12.1994 OpenUrl CrossRef PubMed Web of Science ↵ Perogamvros , L. , Baird , B. , Seibold , M. , Riedner , B. , Boly , M. , & Tononi , G . ( 2017 ). The Phenomenal Contents and Neural Correlates of Spontaneous Thoughts across Wakefulness, NREM Sleep, and REM Sleep . Journal of Cognitive Neuroscience , 29 ( 10 ), 1766 – 1777 . doi: 10.1162/jocn_a_01155 OpenUrl CrossRef PubMed ↵ Peters , E. , Kummer , L. , & Erlacher , D . ( 2024 ). Two-way communication in lucid dreaming using Electrical Muscle Stimulation (EMS) . International Journal of Dream Research , 17 ( 2 ), Article 2 . doi: 10.11588/ijodr.2024.2.106414 OpenUrl CrossRef ↵ Raduga , M. , Zhunusova , Z. , & Shashkov , A . ( 2020 ). Increasing perception vividness during lucid dreams by imagining wakefulness . International Journal of Dream Research , 281 – 284 . doi: 10.11588/ijodr.2020.2.71168 OpenUrl CrossRef ↵ Raichle , M. E . ( 2015 ). The Brain’s Default Mode Network . Annual Review of Neuroscience , 38 ( 1 ), 433 – 447 . doi: 10.1146/annurev-neuro-071013-014030 OpenUrl CrossRef PubMed ↵ Schiller , B. , Heinrichs , M. , Beste , C. , & Stock , A.-K . ( 2021 ). Acute alcohol intoxication modulates the temporal dynamics of resting electroencephalography networks . Addiction Biology , 26 ( 6 ), e13034 . doi: 10.1111/adb.13034 OpenUrl CrossRef PubMed ↵ Schredl , M . ( 2024 ). Differences in lucid dream reports and non-lucid dream reports: A single-case analysis . International Journal of Dream Research , 1 – 7 . doi: 10.11588/ijodr.2024.1.91940 OpenUrl CrossRef ↵ Stumbrys , T. , & Erlacher , D . ( 2017 ). Mindfulness and Lucid Dream Frequency Predicts the Ability to Control Lucid Dreams . Imagination, Cognition and Personality , 36 ( 3 ), 229 – 239 . doi: 10.1177/0276236616683388 OpenUrl CrossRef ↵ Stumbrys , T. , Erlacher , D. , & Malinowski , P . ( 2015 ). Meta-Awareness During Day and Night: The Relationship Between Mindfulness and Lucid Dreaming . Imagination, Cognition and Personality , 34 ( 4 ), 415 – 433 . doi: 10.1177/0276236615572594 OpenUrl CrossRef ↵ Stumbrys , T. , Erlacher , D. , & Schredl , M . ( 2013 ). Reliability and stability of lucid dream and nightmare frequency scales . International Journal of Dream Research , 123 – 126 . doi: 10.11588/ijodr.2013.2.11137 OpenUrl CrossRef ↵ Tarailis , P. , Koenig , T. , Michel , C. M. , & Griškova-Bulanova , I . ( 2023 ). The Functional Aspects of Resting EEG Microstates: A Systematic Review . Brain Topography . doi: 10.1007/s10548-023-00958-9 OpenUrl CrossRef ↵ Tarailis , P. , Šimkutė , D. , Koenig , T. , & Griškova-Bulanova , I . ( 2021 ). Relationship between Spatiotemporal Dynamics of the Brain at Rest and Self-Reported Spontaneous Thoughts: An EEG Microstate Approach . Journal of Personalized Medicine , 11 ( 11 ), 1216 . doi: 10.3390/jpm11111216 OpenUrl CrossRef PubMed ↵ Tomescu , M. I. , Papasteri , C. C. , Sofonea , A. , Boldasu , R. , Kebets , V. , Pistol , C. A. D. , Poalelungi , C. , Benescu , V. , Podina , I. R. , Nedelcea , C. I. , Berceanu , A. I. , & Carcea , I . ( 2022 ). Spontaneous thought and microstate activity modulation by social imitation . NeuroImage , 249 , 118878 . doi: 10.1016/j.neuroimage.2022.118878 OpenUrl CrossRef PubMed ↵ Tzioridou , S. , Dresler , M. , Sandberg , K. , & Mueller , E. M . ( 2022 ). The role of mindful acceptance and lucid dreaming in nightmare frequency and distress . Scientific Reports , 12 ( 1 ), 15737 . doi: 10.1038/s41598-022-19624-4 OpenUrl CrossRef PubMed ↵ Voss , U. , Holzmann , R. , Tuin , I. , & Hobson , A. J . ( 2009 ). Lucid Dreaming: A State of Consciousness with Features of Both Waking and Non-Lucid Dreaming . Sleep , 32 ( 9 ), 1191 – 1200 . doi: 10.1093/sleep/32.9.1191 OpenUrl CrossRef PubMed Web of Science ↵ Voss , U. , Schermelleh-Engel , K. , Windt , J. , Frenzel , C. , & Hobson , A . ( 2013 ). Measuring consciousness in dreams: The lucidity and consciousness in dreams scale . Consciousness and Cognition , 22 ( 1 ), 8 – 21 . doi: 10.1016/j.concog.2012.11.001 OpenUrl CrossRef PubMed ↵ Wiemers , M. C. , Laufs , H. , & von Wegner , F. ( 2023 ). Frequency Analysis of EEG Microstate Sequences in Wakefulness and NREM Sleep . Brain Topography . doi: 10.1007/s10548-023-00971-y OpenUrl CrossRef ↵ Zanesco , A. P. , Denkova , E. , & Jha , A. P . ( 2021 ). Self-reported Mind Wandering and Response Time Variability Differentiate Prestimulus Electroencephalogram Microstate Dynamics during a Sustained Attention Task . Journal of Cognitive Neuroscience , 33 ( 1 ), 28 – 45 . doi: 10.1162/jocn_a_01636 OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted February 14, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following EEG Microstates reveal distinct network dynamics in lucid and non-lucid REM sleep Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. 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Share EEG Microstates reveal distinct network dynamics in lucid and non-lucid REM sleep Xinlin Wang , Emma Peters , Johannes Strelen , Nathan Lockhart , Michael Franklin , Stephen LaBerge , Thomas Koenig , Martin Dresler , Daniel Erlacher bioRxiv 2025.02.12.637792; doi: https://doi.org/10.1101/2025.02.12.637792 Share This Article: Copy Citation Tools EEG Microstates reveal distinct network dynamics in lucid and non-lucid REM sleep Xinlin Wang , Emma Peters , Johannes Strelen , Nathan Lockhart , Michael Franklin , Stephen LaBerge , Thomas Koenig , Martin Dresler , Daniel Erlacher bioRxiv 2025.02.12.637792; doi: https://doi.org/10.1101/2025.02.12.637792 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7622) Biochemistry (17650) Bioengineering (13871) Bioinformatics (41880) Biophysics (21424) Cancer Biology (18566) Cell Biology (25461) Clinical Trials (138) Developmental Biology (13365) Ecology (19866) Epidemiology (2067) Evolutionary Biology (24290) Genetics (15590) Genomics (22475) Immunology (17713) Microbiology (40328) Molecular Biology (17148) Neuroscience (88473) Paleontology (666) Pathology (2827) Pharmacology and Toxicology (4816) Physiology (7635) Plant Biology (15114) Scientific Communication and Education (2044) Synthetic Biology (4286) Systems Biology (9815) Zoology (2268)