ArticleNeuroscience of consciousness2020
The Dream Catcher experiment: blinded analyses failed to detect markers of dreaming consciousness in EEG spectral power.
Article in Neuroscience of consciousness, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 34 citations in OpenAlex.
- Article
- A dream EEG and mentation database.Nature communications · 2025Article
- Wakefulness can be distinguished from general anesthesia and sleep in flies using a massive library of univariate time series analyses.PLoS biology · 2025Article
- Dreaming conundrum.Journal of sleep research · 2025Review
- We are the Sensors of Consciousness! A Review and Analysis on How Awakenings During Sleep Influence Dream Recall.Nature and science of sleep · 2025Article
- Unlocking Dreams and Dreamless Sleep: Machine Learning Classification With Optimal EEG Channels.BioMed research international · 2025Article
- From high- to low-density EEG for automatic classification of dream experiences during stage 2 of NREM.Sleep advances : a journal of the Sleep Research Society · 2025Article
- Do individuals with disorders of consciousness dream and mind wander? Implications for improving diagnosis and understanding patient wellbeing.Neuroscience of consciousness · 2025Article
- Functional connectivity drifts during sleep as a marker of fluctuations in the level of consciousness.Neuroscience of consciousness · 2025Article
- Review
- The vision of dreams: from ontogeny to dream engineering in blindness.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2022Review
- What about dreams? State of the art and open questions.Journal of sleep research · 2022Review
- Theories of consciousness.Nature reviews. Neuroscience · 2022Review
- EEG Lempel-Ziv complexity varies with sleep stage, but does not seem to track dream experience.Frontiers in human neuroscience · 2022Article
- Investigation on Neurobiological Mechanisms of Dreaming in the New Decade.Brain sciences · 2021Review
- How deep is the rift between conscious states in sleep and wakefulness? Spontaneous experience over the sleep-wake cycle.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Article
- EEG Signal Diversity Varies With Sleep Stage and Aspects of Dream Experience.Frontiers in psychology · 2021Article
- The Neural Correlates of Dreaming Have Not Been Identified Yet. Commentary on "The Neural Correlates of Dreaming. Nat Neurosci. 2017".Frontiers in neuroscience · 2020Article
- Article
- Does the Mind Wander When the Brain Takes a Break? Local Sleep in Wakefulness, Attentional Lapses and Mind-Wandering.Frontiers in neuroscience · 2019Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Dream Catcher test defines the criteria for a genuine discovery of the neural constituents of phenomenal consciousness. Passing the test implies that some patterns of purely brain-based data directly correspond to the subjective features of phenomenal experience, which would help to bridge the explanatory gap between consciousness and brain. Here, we conducted the Dream Catcher test for the first time in a step-wise and simplified form, capturing its core idea. The Dream Catcher experiment involved a Data Team, which measured participants' brain activity during sleep and collected dream reports, and a blinded Analysis Team, which was challenged to predict, based solely on brain measurements, whether or not a participant had a dream experience. Using a serial-awakening paradigm, the Data Team prepared 54 1-min polysomnograms of non-rapid eye movement sleep-27 of dreamful sleep and 27 of dreamless sleep (three of each condition from each of the nine participants)-redacting from them all associated participant and dream information. The Analysis Team attempted to classify each recording as either dreamless or dreamful using an unsupervised machine learning classifier, based on hypothesis-driven, extracted features of electroencephalography (EEG) spectral power and electrode location. The procedure was repeated over five iterations with a gradual removal of blindness. At no level of blindness did the Analysis Team perform significantly better than chance, suggesting that EEG spectral power could not be utilized to detect signatures specific to phenomenal consciousness in these data. This study marks the first step towards realizing the Dream Catcher test in practice.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.