Thus, while the frequent lucid dream group showed increased functional connectivity of left aPFC with regions of IPL and MTG that overlapped with this FPCN subsystem, there was no difference in the average connectivity of this subsystem between groups.
To evaluate whole-brain differences in network and topological properties, we next parcellated the brain into regions according to the Lausanne atlas 37 , 38 and performed graph-theoretic analysis. Graphs were thresholded over a range of connection densities 0. Multiple comparisons were corrected against a max t distribution across all nodes in the network see Methods: Graph-theoretic network analysis. Whole-brain graph-theoretic network differences between frequent lucid dream and control groups.
Right panel: boxplots of area under the curve AUC for frequent lucid dream and control groups. The bottoms and tops of the boxes show the 25th and 75th percentiles the lower and upper quartiles , respectively; the inner white band shows the median; and the whiskers show the most extreme data points not considered outliers outliers are plotted separately with red squares. To the best of our knowledge, the current study is the first to evaluate differences in brain structure and functional connectivity of individuals who experience lucid dreams with high frequency.
We found that compared to a control group matched on age, gender and dream recall frequency, individuals who reported lucid dreams spontaneously approximately every other night or more had increased resting-state functional connectivity between the left anterior prefrontal cortex aPFC and the bilateral angular gyrus AG , bilateral middle temporal gyrus MTG and right inferior frontal gyrus IFG.
The frequent lucid dream group also showed decreased functional connectivity between left aPFC and bilateral insula. Whole-brain graph-theoretic analysis revealed that left aPFC had increased node degree and strength in the frequent lucid dream group compared to the control group. In contrast to these functional changes, we did not observe any differences in brain structure gray matter density in any brain area between groups c. Furthermore, no differences were observed between frequent lucid dream and control groups in behavioral or questionnaire measures of working memory capacity, prospective memory, mind-wandering frequency or trait mindfulness.
The current results suggest that increased functional integrity during wakefulness between aPFC and temporoparietal association areas—all regions that show suppressed activity in REM sleep and increased activity during lucid REM sleep—is associated with the tendency to have frequent lucid dreams.
Becoming lucid during REM sleep dreaming involves making an accurate metacognitive judgment about the state of consciousness one is in, often by recognizing that the correct explanation for an anomaly in the dream is that one is dreaming 1 , 2. Given the link to metacognition, it has been speculated that lucid dreaming is linked to neural systems that regulate executive control processes, in particular the frontoparietal control network FPCN 27 , The FPCN is a large-scale brain network that is interconnected with both the default mode network DMN , which is linked to internal aspects of cognition, such as autobiographical memory 43 , 44 , spontaneous thought 45 , 46 , and self-referential processing 47 , and the dorsal attention network DAN , which is involved in visuospatial perceptual attention 48 , Being spatially interposed between these two systems, the FPCN is postulated to integrate information coming from the opposing DMN and DAN systems by switching between competing internally and externally directed processes Based on a parcellation of 17 resting-state networks in the human brain, which distinguished potentially separable FPCN networks 35 , a recent study found that the FPCN could be fractionated using hierarchical clustering and machine learning classification into two distinct subsystems: FPCNa, which is more strongly connected to the DMN than the DAN and is linked to introspective processes, and FPCNb, which is more strongly connected to the DAN than the DMN and is linked to regulation of perceptual attention The current results show that frequent lucid dreams are associated with increased functional connectivity between aPFC and a network of regions that showed substantial overlap with the FPCN sub-network corresponding most closely to FPCNa 35 , However, neither connectivity within FPCN broadly defined through meta-analysis nor connectivity within FPCN sub-networks as defined through parcellation of resting-state networks was significantly associated with frequent lucid dreaming in the current study.
This may be attributed to both the partial overlap of the regions that showed increased aPFC connectivity in lucid dreamers with FPCN networks, as well as the fact that lucid dream frequency was associated with increased connectivity between these regions and aPFC in the left hemisphere, but not to increased connectivity between these regions and right aPFC, or broadly increased connectivity between other regions of FPCN to each other outside of aPFC.
The strongest increase in functional connectivity in the frequent lucid dream group was observed between left aPFC and IPL, which localized to a dorsal segment of the anterior subdivision of the angular gyrus PGa bilaterally, as measured by overlap with cytoachitectonic probability maps. While many neuroimaging studies have treated the regions that comprise IPL as a homogenous region, cytoarchitectonic mapping studies have shown that these regions can be subdivided 51 , 52 , and these subdivisions show distinct patterns of structural and functional connectivity Specifically, PGa shows increased functional connectivity with the caudate, anterior cingulate, and bilateral frontal poles compared to PGp, whereas PGp shows increased connectivity with regions of the DMN, including precuneus, medial prefrontal cortex and parahippocampal and hippocampal gyri Cognitive or clinical correlates of altered functional connectivity between the frontal pole and this specific subdivision of AG PGa have to our knowledge not yet been identified, since much of the cognitive neuroscience literature on this region lacks anatomical specificity.
However, a meta-analysis of neuroimaging studies of language and semantic processes found that the left AG had the densest concentration of activation foci across studies, with a significant clustering of activation foci also in MTG The authors also note that these regions are greatly expanded in humans compared to non-human primates, suggesting a role in the development of language.
Moreover, PGa is more closely linked to the semantic system that PGp, and analysis of the connectivity and cognitive functions associated with this region suggests that it is positioned at the top of a processing hierarchy for concept retrieval and conceptual integration Specifically, non-lucid dreams exhibit reduced working memory function, reduced ability to engage in behavioral control and planning, and reduced reflective consciousness 55 , 56 , The distinction between primary and higher-order consciousness is thought to depend on the linguistic abilities that separate humans from other species While language processes also occur during non-lucid dreams 60 , 61 , they are nevertheless linked to the remembered present and apparently lack the conceptual structure that allows for full self-awareness.
The measurement of individual differences in lucid dream frequency has been done in inconsistent ways and could be improved in future research. Indeed, lucid dreams can range from a realization about the fact that one is dreaming followed by a loss of lucidity shortly thereafter to more extended lucid dreams in which an individual can maintain lucidity for prolonged periods of time Likewise, lucid dreams can be characterized by varying degrees of clarity of thought.
Evaluating the duration of lucid dreams as well as the degree of awareness during lucid dreams will be valuable to relating brain structural and functional measures to lucid dream frequency in future studies. An extended discussion of this issue is beyond the scope of the present article; however, overall these remarks emphasize the need for the development of standardized measures that can be used to assess individual differences in frequency of lucid dreams that simultaneously measure the duration and degree of lucidity during dreams.
Another limitation of the current study is that our measurement of lucid dream frequency relied on questionnaire responses and participant interviews. There are established methods for the objective validation of individual lucid dreams in a sleep laboratory setting using volitional eye-movement signals 4 , but there are no protocols for physiologically validating the frequency of lucid dreams. While questionnaire measures of lucid dream frequency have shown high test-retest reliability 64 , one way to further validate participant questionnaire responses would be to attempt to physiologically validate at least one lucid dream in the sleep laboratory for each participant.
We think that additional validations such as this would potentially be valuable to incorporate in future studies. Nevertheless, it is important to note that the estimated frequency of lucid dreaming would still depend on questionnaire assessment. Thus, approaches such as this do not obviate the reliance on questionnaire assessment as used in the current study. An intriguing, though ambitious, method for deriving a measure of lucid dream frequency not dependent on questionnaire assessment would be to utilize home-based EEG recording systems to collect longitudinal sleep polysomnography data, from which estimates of lucid dreaming frequency could be derived from the frequency of signal-verified lucid dreams collected over many nights.
However, this approach would only measure the frequency of signal-verified lucid dreams, and instances in which participants achieved lucidity but did not make the eye signal due to factors such as awakening or forgetting the intention would be missed by this procedure. In contrast to the observed differences in functional connectivity described above, in the current study we did not observe any significant differences in brain structure gray matter density between groups.
As noted in the introduction, a limitation of that study is that the high-lucidity group was not a sample of frequent lucid dreamers, but rather individuals from a database that scored above the group median on a composite measure of dreaming, which measured not only frequency of lucid dreams but also different dimensions of dream content.
However, the fact that the study found that these aPFC regions also showed increased BOLD activity during the monitoring component of a thought-monitoring task lends additional plausibility to the results.
It is important to note that issues of statistical power could also account for the discrepant findings of these two studies. Unfortunately, no statistics or estimates of effect size have been reported for this effect and as a result we were unable to perform a power analysis to determine the adequate sample size for testing this effect.
However, a single study that fails to reject the null hypothesis does not provide good evidence for the absence of an effect, especially with relatively small sample sizes. Overall, therefore, more research addressing this question using larger sample sizes will be needed before firm conclusions can be drawn. Here we studied individuals who reported spontaneous lucid dreaming with high frequency without engaging in training to have lucid dreams.
In our questionnaire samples, the proportion of individuals who reported spontaneous lucid dreams on close to a nightly basis constituted approximately 1 in 1, respondents. While frequent spontaneous lucid dreams are uncommon, evidence indicates that lucid dreaming is a learnable skill that can be developed by training in strategies such as metacognitive monitoring i. While it is plausible that the neurophysiological correlates of spontaneous frequent lucid dreaming are the same as frequent lucid dreaming that occurs as a result of training, this has not yet been studied.
Future longitudinal training studies would be valuable in order to evaluate within-subject changes in brain connectivity as a result of training to have lucid dreams and to compare how such changes relate to the functional network associated with frequent lucid dreaming identified here.
No significant differences were observed between groups in working memory capacity, or questionnaire assessments of prospective memory or trait mindfulness. It has been suggested that a sufficient level of working memory is required in order to become lucid during dreaming sleep 2 and thus it might be predicted that frequent lucid dreams could be associated with a higher baseline level of working memory capacity. Likewise, an effective method of lucid dream induction, the Mnemonic Induction of Lucid Dreams MILD technique 63 , relies on the use of prospective memory to become lucid, and thus it might be predicted that frequent lucid dreams could be associated with increased prospective memory ability.
However, spontaneous frequent lucid dreamers neither necessarily need to activate a pre-sleep intention nor use prospective memory to remember to recognize that they are dreaming; instead, their lucid dreams tend to occur spontaneously without engaging in specific methods for inducing them.
Thus, it remains plausible that there could be a relationship between working memory and prospective memory and successful training in lucid dreaming despite a lack of a relationship between these variables and spontaneous frequent lucid dreams. In future work it would be interesting to explore whether individuals with higher baseline scores on these measures show increased propensity in successfully training to have lucid dreams, as well as to quantify the association between potential improvements in these skills and lucid dream frequency as a result of training.
Finally, the finding that there was no significant difference in mindfulness in frequent lucid dreamers is consistent with other research, which has found that outside of meditators, there does not appear to be an association between trait mindfulness and lucid dream frequency in the facets of mindfulness studied here decentering and curiosity 34 , 67 , If so, this would suggest that it may be possible to bias these networks toward increased metacognitive awareness of dreaming during REM sleep, for example through techniques to increase activation of these regions.
Notably, a recent double blind, placebo-controlled study found that cholinergic enhancement with galantamine, an acetylcholinesterease inhibitor AChEI , increased the frequency of lucid dreams in a dose-related manner when taken late in the sleep cycle and combined with training in the mental set for lucid dream induction While the relationship between cholinergic modulation and frontoparietal activation is complex and depends on the task context and population under study see ref. Given that frontoparietal activity is typically suppressed during REM sleep, an intriguing follow-up to these findings based on the current results would be to examine whether AChEIs, and galantamine in particular, may facilitate lucid dreaming through increasing activation within the network of fronto-temporo-parietal areas observed here.
In line with the above ideas, several studies have attempted to induce lucid dreams through electrical stimulation of the frontal cortex during REM sleep. One study tested whether transcranial direct current stimulation tDCS applied to the frontal cortex would increase lucid dreaming While tDCS resulted in a small numerical increase in self-ratings of the unreality of dream objects, it did not significantly increase the number of lucid dreams as rated by judges or confirmed through the eye-signaling method.
Specifically, lucid dreams were not dreams that participants self-reported as lucid, nor dreams that were objectively verified to be lucid through the eye-movement signaling method. Instead, dreams were inferred to be lucid based on higher scores to questionnaire items measuring the amount of insight or dissociation Given that dissociation i.
Furthermore, mean ratings in the insight subscale increased from approximately 0. In summary, it remains unclear whether electrical brain stimulation techniques could be effective for inducing lucid dreams see refs 19 , 62 for further discussion. Nevertheless, given the current findings, stimulation of aPFC and temporoparietal association areas appears to be a worthwhile direction for future research attempting to induce lucid dreaming.
Future studies might consider testing a wider range of stimulation parameters, particularly applied to aPFC, as well as combining stimulation with training in the appropriate attentional set for lucid dream induction. Participants were recruited via mass emails sent to University of Wisconsin-Madison faculty, staff and students. The study was described broadly as a study on brain structure and dreaming. Exclusion criteria for all participants included pregnancy, severe mental illness or any contraindications for MRI e.
To determine study eligibility, participants completed a questionnaire that measured their dream recall and lucid dreaming frequency described below. For the frequent lucid dream group, we recruited individuals who reported a minimum of 3—4 lucid dreams per week, or approximately one lucid dream every other night without engaging in training to have lucid dreams.
We recruited control participants who were 1-to-1 matched to participants in the frequent lucid dream group on age, gender and dream recall frequency variables but who reported lucid dreams never or rarely. Signed informed consent was obtained from all participants before the experiment, and ethical approval for the study was obtained from the University of Wisconsin—Madison Institutional Review Board.
The study protocol was conducted in accordance with the Declaration of Helsinki. Participants completed a questionnaire that measured their dream recall and lucid dreaming frequency Supplementary Methods: Dream and lucid dream frequency questionnaire. Dream recall was measured with a pt scale ranging from 0 never to 15 more than one dream per night.
Lucid dream frequency was measured with a pt scale ranging from 0 no lucid dreams to 15 multiple lucid dreams per night. Participants were also provided with a short excerpt of a written report of a lucid dream see Supplementary Methods for full text of the definition and example of lucid dreaming provided on the questionnaire measure.
Several additional checks were made to ensure that participants had a clear understanding of the meaning of lucid dreaming. First, participants were asked to provide a written example of one of their lucid dreams, including how they knew they were dreaming.
Second, participants were interviewed by the experimenters before being enrolled in the study to ensure that they had a clear understanding of the meaning of lucid dreaming. During the interview participants described several recent lucid dreams and confirmed the frequency with which they experienced lucid dreams through follow-up questions. Only participants who demonstrated unambiguous understanding of lucidity and met the frequency criteria as confirmed by both written and oral responses were enrolled in the frequent lucid dream group.
The frequent lucid dream group also reported several additional variables related to their experiences with lucid dreaming, including the number of lucid dreams they had in the last six months, the most lucid dreams they had ever had in a six-month period, whether they had engaged in training to have lucid dreams and their general interest in the topic.
As noted above, we aimed to match dream recall between the frequent lucid dream group and control group as closely as possible in order to control for this potentially confounding variable. However, it was not always possible to recruit a matched control participant that was exactly matched on age, gender and dream recall.
For each participant in the frequent lucid dream group, we therefore sought to recruit the closet matched pair control participant of the same age and gender, with the constraint that dream recall had to be within at least 3 rank order values on the questionnaire measure.
In 7 cases, we were able to obtain an exact match between control participants and frequent lucid dream participants on dream recall, in 5 cases within 1 rank value, in 1 case within 2 rank values and in 1 case within 3 rank values. In 4 out of the 5 cases that were within 1 rank value, the difference in reported dream recall frequency was between 7 dreams recalled per week and 5—6 dreams recalled per week, and in the remaining case the difference was between 3—4 dreams recalled per week and 5—6 dreams recalled per week.
Overall this method ensured that the frequent lucid dream group and control group were closely matched on dream recall frequency. Participants completed several additional assessments that measured cognitive variables which have been hypothesized to be associated with lucid dreaming and have been linked to PFC function, including working memory capacity WMC , trait mindfulness and prospective memory e.
These tasks have been validated to yield a reliable measure of WMC 75 , In brief, each task presents to-be-remembered stimuli in alternation with an unrelated processing task. Following standard scoring procedures, span scores were calculated as the total number of items recalled in correct serial order across all trials Participants also completed a questionnaire battery that assessed several additional variables of interest: their mind-wandering frequency, memory function in everyday life and trait mindfulness.
Memory function was assessed with the Prospective and Retrospective Memory Questionnaire PRMQ 78 , which measures self-report scores of the frequency of both prospective and retrospective memory errors in everyday life see ref. The TMS measures two factor-analytically derived components of mindfulness: Curiosity and Decentering.
Resting-state functional MRI scans were collected on a 3. During the resting-state scan, participants were instructed to stay awake and relax, to hold as still as possible, and to keep their eyes open. A diffeomorphic non-linear registration algorithm diffeomorphic anatomical registration through exponentiated lie algebra; DARTEL 81 was used to iteratively register the images to their average.
The resulting flow fields were combined with an affine spatial transformation to generate Montreal Neurological Institute MNI template spatially normalized and smoothed Jacobian-scaled gray matter images. We additionally evaluated average gray matter density between groups in the two regions of prefrontal cortex and bilateral hippocampus observed by ref. Total hippocampal volume was also extracted from an updated routine for automated segmentation of the hippocampal subfields implemented in FreeSurfer version 6.
Resting-state fMRI data were processed based on a workflow described previously To remove potential scanner instability effects, the first four volumes of each EPI sequence were removed.
Brain mask, cerebrospinal fluid CSF mask and white matter WM mask were parcellated using FreeSurfer 87 , 88 , 89 , 90 and transformed into EPI space and eroded by 2 voxels in each direction to reduce partial volume effects.
Realigned timeseries were masked using the brain mask. Differences in global mean intensity between functional sessions were removed by normalizing the mean of all voxels across each run to This was followed by nuisance regression of motion-related artifacts using a GLM with six rigid-body motion registration parameters and outlier scans as regressors. Principal components of physiological noise were estimated using the CompCor method Timeseries were then denoised using a GLM model with 10 CompCor components as simultaneous nuisance regressors.
Note that global signal regression was not performed because this processing step can induce negative correlations in group-level results Although aPFC functional connectivity was the main target of the current investigation, we also performed supplementary seed-based functional connectivity analysis on other regions identified in ref.
Translated ROIs were restricted within the cortical ribbon mask. Full brain connectivity correlation maps were calculated using AFNI Voxelwise independent samples t -tests were performed between groups. Whole-brain analyses were conducted, correcting for multiple comparisons using topological FDR 93 at the cluster level.
Cytoarchitectonic mapping studies have shown that AG can be divided into anterior PGa and posterior PGp subdivisions and IPS can be divided into three distinct subdivisions hlP1 on the posterior lateral bank, hlP2 which is anterior to hIP1, and hlP3 which is posterior and medial to both subdivisions 51 , The subdivisions of AG and IPS have been shown to have distinct structural and functional connectivity patterns We performed a follow-up analysis on the functional clusters identified in our seed based functional connectivity analysis in order to characterize the overlap between these clusters and the anatomical subdivisions of these regions.
MPMs create non-overlapping regions of interest from the inherently overlapping cytoarchitectonic probability maps 94 , The anatomical boundaries of these maps are described in detail in previous publications 51 , 52 , Mean connectivity values from each binarized mask were exacted using the MarsBar toolbox In order to compare whether connectivity within and between established large scale resting-state brain networks showed differences between groups, we extracted timecourses from a set of nodes from a meta-analysis by Power, et al.
For each network, we calculated the mean correlation between all nodes within the network within-network connectivity as well as the mean correlation between all nodes of a given network and all the nodes of each other network between-network connectivity. We also evaluated the overlap between our seed-based functional connectivity results and a network parcellation of human brain connectivity networks We followed up this network overlap analysis by evaluating the connectivity between all nodes within the frontoparietal control subsystem that showed the largest overlap with the functional connectivity results, based on a node parcellation of the 17 functional networks To construct functional networks for graph-theoretic analysis, anatomical scans were segmented using FreeSurfer and parcellated into regions according to the Lausanne atlas included in the connectome mapping toolkit 37 , Resting-state fMRI data pre-processing was identical to the procedures described above see Resting-state fMRI data processing with the exception that no spatial smoothing was applied, as spatial smoothing can distort network measures derived from average timeseries within parcellated regions e.
All network metrics were computed in Matlab v 9. For each node in the network we analyzed the degree k , strength s , betweenness centrality BC and eigenvector centrality EC.
These metrics are described in detail elsewhere see refs 98 , 99 for reviews. In brief, k quantifies the total number of connections of a node, while s quantifies the sum of the weights of all connections to a node.
BC and EC are different measures of centrality of nodes: BC is the fraction of all shortest paths in the network that contain a given node and EC quantifies nodes connected to other densely connected nodes as having high centrality.
In order to compare network and topological properties between groups it is important to ensure that graphs contain the same number of edges Following recommended practice 99 , rather than apply a single threshold to graphs, which would limit any findings to a single arbitrary connection density, we thresholded graphs over a range of connection densities 0.
To test the null hypothesis of no difference in AUC between groups, we used a nonparametric bootstrapping procedure in which we randomly reassigned groups with replacement 10, times and computed a bootstrapped t -value for each node. This statistical approach has been used in previous studies and allows for strong control over type I error , The data that support the findings of this study are available from the corresponding author on reasonable request.
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Motor Skills 52 , — Erlacher, D. Motor area activation during dreamed hand clenching: A pilot study on EEG alpha band. Sleep Hypnosis 5 , — Google Scholar. Voluntary control of respiration during REM sleep. Sleep Res. Psychophysiological correlates of the initiation of lucid dreaming.
Here's how it works:. Waggoner notes it helps to do this practice consistently each night before sleep. One Stanford researcher found that it could help him achieve 18 to 26 lucid dreams per month , with up to four per night, compared to the 13 per month he experienced using suggestion alone, and less than one per month he had without technique.
Heads up: MILD does require good dream recall , but here's how it's done:. And lastly, as Ellis and Waggoner both note, if your desire comes true and you do become lucid in a dream, don't freak out.
While lucid dreaming can be powerful and fun, to say the least, it does come with some risks—especially if you have any sort of mental health disorder psychosis, dissociation, and depression, in particular.
As Waggoner adds, he always tells people, "If you can not handle waking reality, then it seems best to avoid lucid dreaming. But if you can handle waking reality, then it seems generally safe to dream lucidly. However, according to Ellis, even healthy dreamers can have trouble waking up out of a lucid dream sometimes, "and experience a series of 'false awakenings' or will enter a black void before they are able to orient fully to the here and now.
And because lucid dreaming isn't a typical sleep state, she adds, some dream experts believe too much lucidity can interrupt one's normal sleep cycle in an unhealthy way.
According to Waggoner, the name of the game is to simply "wait until you feel relatively at peace with your waking state experience before beginning a lucid dream practice. Lucid dreaming is an undoubtedly fascinating, and for many, eye-opening, experience. While there are some who would do best to avoid it, if you're at peace with your life, you should be fine to give the practice a go by setting the right intentions.
Happy dreaming! Want your passion for wellness to change the world? Become A Functional Nutrition Coach! Enroll today to join our upcoming live office hours. You are now subscribed Be on the lookout for a welcome email in your inbox! Main Navigation. Want To Lucid Dream Tonight? Log in Profile. Saved Articles. Contact Support. Log Out. Your cart is empty. Our online classes and training programs allow you to learn from experts from anywhere in the world.
Explore Classes. Our editors have independently chosen the products listed on this page. If you purchase something mentioned in this article, we may earn a small commission. Last updated on August 24, In This Article. What are lucid dreams, and why would I want one? Frequently test reality. Get more sleep to make dreams more likely. Use the power of suggestion. For example, a person with social anxiety might use the dream to play out different social situations, allowing themselves to practice engaging with others and see that nothing scary happens.
After practicing in the lucid dream, they might feel bolder about trying those same techniques in the real world. Lucid dreamers are also able to open up their minds to be more creative, by exploring the dreams that they experience. By taking agency and making active decisions through the dream, rather than passively experiencing them, they can make creative connections and test how things work.
Lucid dreaming occurs during the REM stage of sleep. During REM , most of our muscles become paralyzed, in order to prevent us from injuring ourselves while acting out our dreams, However, our eye muscles are still able to move. Since the movement matched their real eyes, researchers were able to study the impact to brain waves and other biological functions while they slept.
Lucid dreaming takes time and practice to learn. By regularly practicing the following techniques, you can train your brain to lucid dream.
Dreams occur during REM, the last stage of your sleep cycle which occurs in increasing amounts during the second half of the night. To enjoy more dreams, you need to enjoy more restful sleep to ensure you get as much REM as possible. Good news: REM is also associated with better memory, improved focus, and greater emotional regulation! To get better sleep, follow good sleep hygiene. Keep your bedroom as dark, cool, and quiet as possible. Use blackout curtains or an eye mask to block out any ambient light.
Use ear plugs or a white noise machine to do the same with noise. Set the thermostat to a cool mid degrees Fahrenheit. Finally, before you go to bed, follow a calming bedtime routine. Engage in restful activities like unplugging from your electronics , taking a warm bath, or practicing aromatherapy or meditation.
The first step to successful lucid dreaming is tuning in to your dreams. Keep a dream journal by your bed, and the moment you wake up, write down everything you remember from your dream.
If you think faster than you write, try recording your memories as a voice memo on your phone. In addition to letting you record your dreams, the value of these apps over traditional pen and paper is that they allow you to search your dream notes for recurring themes, symbols, and characters—which brings us to our next step.
Review your dream journal regularly and look for any patterns. Do certain themes or people show up again and again? These may provide insights into the types of issues your inner psyche is focused on. Keep repeating it until you fall asleep. When you wake up from a dream, stay in bed as you write down anything you remember in your dream journal.
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