Lucid dream induction with sleep EEG wearables
Bibliographic record
Abstract
Lucid dreaming (LD) is defined as a state of awareness of the ongoing dream state while sleeping. Lucid dreaming is a rather rare phenomenon; however, it can be learned and trained, and various studies have proposed different techniques to ‘induce’ lucid dreams. Nonetheless, these studies either lacked physiological measurements and were therefore merely limited to self-reported questionnaires, or in the case of including physiological measurements, their generalizability was restricted mainly due to the exclusive recruitment of ‘experienced’ lucid dreamers. Only a few studies attempted to reliably induce lucid dreams in ‘naive’ participants, but they involved small sample sizes and have not yet been replicated. To overcome these limitations, we designed a multi-center study including three laboratories, in the Netherlands, Canada, and Italy respectively, with the aim of recruiting 60 participants overall (i.e. 20 participants per laboratory). This is the largest sample size for a lucid dreaming induction study with physiological measurements to date. We will test the applicability of a combination of two lucid dreaming induction techniques: targeted lucidity reactivation (TLR) and sense-initiated lucid dream (SSILD), which will be implemented by presenting perceptual cues (visual, auditory, and tactile) before and during REM sleep. To do so, we will employ minimal measurement modalities, i.e., an EEG headband and three additional chin EMG electrodes. We will also use this dataset to develop and validate the first open-source dream engineering toolbox, Dreamento (DREAM ENgineering TOolbox, Esfahani et al., 2022). Participants will visit the laboratory three times throughout an approximately two week period, including an intake session and two morning naps (stimulation and control, in counterbalanced order across subjects). During the intake session, participants will receive information about the study and complete preliminary screening questionnaires. Then, participants will complete daily dream diaries for the following two weeks. The morning nap sessions will be held at least one and two weeks after the intake session, respectively. Both nap sessions consist of the same cognitive training procedure during wakefulness, but differ in terms of the sensory stimulation procedure during sleep. Participants will receive sensory cues upon detection of REM sleep during the stimulation session, but not during the control session. They will be instructed to signal their lucidity using a predefined intentional eye movement pattern (left-right-left-right, LRLR) and will be awakened once the REM period ends to report any subjective experience and complete a lucidity questionnaire.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".