Highly effective verified lucid dream induction using combined cognitive-sensory training and wearable EEG: a multi-centre study
Bibliographic record
Abstract
Lucid dreaming occurs when one is aware of dreaming while asleep. While lucid dreaming can occur spontaneously, it remains rare. This multi-center study (Netherlands, Italy, Canada) tested a combined induction approach integrating senses-initiated lucid dreaming (SSILD) with targeted lucidity reactivation in a large sample of participants with varying lucid dreaming experience. Sixty participants (33 F; 26.5 ±6.2 years old) completed two morning naps in the sleep laboratory. Participants received pre-sleep SSILD training paired with visual, auditory, and tactile cues that were then reintroduced in REM sleep, with stimulation and sham conditions counterbalanced. Lucidity and cue perception were verified using intentional eye movements from within the dream (signal-verified lucid dream, SVLD). SVLDs occurred in 31 participants (51.7%) across both naps. Subjective lucidity occurred in 63 naps (52.5%), including 40 SVLDs (33.3%) with no difference between stimulation (38.3%) and sham (28.3%) conditions. However, SVLD duration and number of predefined eye movements were higher with sensory cueing. Cues were perceived within sleep in most cued REM periods (71.1%), sometimes acting as lucidity signals (39.1% of stim SVLDs) and occasionally disrupting sleep (23.7% of cued periods). Overall, our combined cognitive-sensory induction method produced relatively high lucid dreaming rates, even in participants who rarely experience them. While cues were sometimes perceived as lucidity signals and may help prolong lucid episodes, our results do not show clear benefits of REM cueing over SSILD training alone. Further refinement of these methods may support clinical applications and deepen insight into consciousness and sensory processing during sleep.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".