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Record W4410502977 · doi:10.1093/sleep/zsaf090.0459

0459 Targeted Dream Incubation to Enhance Creativity and Reduce PTSD Symptoms Using an EEG-Based Software Platform

2025· article· en· W4410502977 on OpenAlexaboutno aff
Robert Stickgold, Alon Shamir, Noa Brande-Eilat, Karen Ginat, Miri Bar-Halpern, Shiran Simonov, Adam Horowitz, Laila Stedman, Shai Kalev, Alex Amirhov, Andrés Arciniegas, Б. М. Коган, Kuan-Jung Chiang, Eitan Kay, Dan Furman

Bibliographic record

VenueSLEEP · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsnot available
Fundersnot available
KeywordsElectroencephalographyCreativityPsychologySoftwareDreamClinical psychologyComputer scienceAudiologyPsychiatryMedicineArtificial intelligenceNeuroscienceSocial psychology

Abstract

fetched live from OpenAlex

Abstract Introduction Recent studies and anecdotal reports suggest that sleep-onset hypnagogic dreams can contain creative insights, in some cases leading to Nobel prizes. While such reports are rare, targeted dream incubation (TDI) can induce dreams on pre-selected topics, such as “a tree,” in 70% of dream reports collected shortly after sleep onset, as well as a subsequent increase in creativity around the targeted topic. Here we describe a new software platform for TDI and plans to use this platform to increase creativity around topics of concern for individuals suffering from PTSD following the October 7th terrorist attacks in Israel. Methods “Arctop Sleep” is a mobile app built on an Application Programming Interface developed by Arctop, Inc. (Los Angeles, CA), that allows communication between the app and an EEG headband. Here we used the dry electrode 4-channel Muse EEG headband (interaXon, Toronto). The app connects wirelessly with the headband, identifies sleep onset in real time, plays a prerecorded message to awaken the user and, once awakened, prompts them to dictate a dream report. Users are then prompted to return to sleep while thinking about the chosen topic. The app produces serial awakening with the user awakening, reporting, and returning to sleep repeatedly, for a 90-minute time interval. Results Early pilot studies have demonstrated successful integration with the EEG headband, with accurate detection of sleep onset in real time (verified by clinically scored PSG). Serial awakenings and dream reports were successfully recorded for participants with PTSD. Analysis of these reports revealed both direct and metaphorical references to the preselected topic. Furthermore, participants showed an improvement in creativity and resilience related to the preselected topic on post-sleep tasks compared to baseline measures. Users reported the process was comfortable, with minimal complaints. Conclusion The Arctop Sleep platform demonstrates promising usability and potential for leveraging targeted dream incubation to enhance creativity around specific topics. This innovative approach has direct applications for individuals experiencing PTSD, and, by fostering constructive engagement with challenging topics in a controlled sleep environment, could facilitate better sleep and more effective cognitive and emotional processing of trauma memories. Support (if any) Support provided by Arctop, Inc.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.326
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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