The Dream Catcher experiment: blinded analyses failed to detect markers of dreaming consciousness in EEG spectral power

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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The Dream Catcher experiment: Blinded analyses disconfirm markers of dreaming consciousness in EEG spectral power

10.1101/643593 ◽

2019 ◽

Author(s):

William Wong ◽

Valdas Noreika ◽

Levente Móró ◽

Antti Revonsuo ◽

Jennifer Windt ◽

...

Keyword(s):

Spectral Power ◽

Brain Activity ◽

Conscious Experience ◽

Phenomenal Consciousness ◽

Nrem Sleep ◽

List Type ◽

Data Team ◽

Eeg Spectral Power ◽

Core Idea ◽

Better Than

AbstractThe 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 graded and simplified form, capturing its core idea. The experiment involved a Data Team, who measured participants’ brain activity during sleep and collected dream reports, and a blinded Analysis Team, who was challenged to predict better than chance, based solely on brain measurements, whether or not a participant had a dream experience. Using a serial-awakening paradigm, the Data Team prepared 54 one-minute polysomnograms of NREM sleep—27 of dreamful sleep (3 from each of the 9 participants) and 27 of dreamless sleep—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 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 does not carry any signatures of phenomenal consciousness. Furthermore, we demonstrate an outright failure to replicate key findings of recently reported correlates of dreaming consciousness.HighlightsThe first reported attempt of the Dream Catcher test.The correlates of conscious experience may not lie in EEG spectral power.Reported markers of NREM dreaming consciousness misperformed in a blinded setting.Those markers also could not be confirmed in an unblinded setting.

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An Analysis of the External Validity of EEG Spectral Power in an Uncontrolled Outdoor Environment during Default and Complex Neurocognitive States

Brain Sciences ◽

10.3390/brainsci11030330 ◽

2021 ◽

Vol 11 (3) ◽

pp. 330

Author(s):

Dalton J. Edwards ◽

Logan T. Trujillo

Keyword(s):

External Validity ◽

Spectral Power ◽

Brain Activity ◽

Outdoor Environment ◽

Oscillatory Activity ◽

Brain State ◽

Beta Band ◽

Eeg Spectral Power ◽

Eeg Power ◽

Human Participants

Traditionally, quantitative electroencephalography (QEEG) studies collect data within controlled laboratory environments that limit the external validity of scientific conclusions. To probe these validity limits, we used a mobile EEG system to record electrophysiological signals from human participants while they were located within a controlled laboratory environment and an uncontrolled outdoor environment exhibiting several moderate background influences. Participants performed two tasks during these recordings, one engaging brain activity related to several complex cognitive functions (number sense, attention, memory, executive function) and the other engaging two default brain states. We computed EEG spectral power over three frequency bands (theta: 4–7 Hz, alpha: 8–13 Hz, low beta: 14–20 Hz) where EEG oscillatory activity is known to correlate with the neurocognitive states engaged by these tasks. Null hypothesis significance testing yielded significant EEG power effects typical of the neurocognitive states engaged by each task, but only a beta-band power difference between the two background recording environments during the default brain state. Bayesian analysis showed that the remaining environment null effects were unlikely to reflect measurement insensitivities. This overall pattern of results supports the external validity of laboratory EEG power findings for complex and default neurocognitive states engaged within moderately uncontrolled environments.

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Alprazolam-induced EEG spectral power changes in rhesus monkeys: a translational model for the evaluation of the behavioral effects of benzodiazepines

Psychopharmacology ◽

10.1007/s00213-021-05793-z ◽

2021 ◽

Vol 238 (5) ◽

pp. 1373-1386

Author(s):

Lais F. Berro ◽

John S. Overton ◽

Jaren A. Reeves-Darby ◽

James K. Rowlett

Keyword(s):

Rhesus Monkeys ◽

Spectral Power ◽

Behavioral Effects ◽

Translational Model ◽

Eeg Spectral Power

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Information-based decoding of the coupling among human brain activity and movement paths

Technology and Health Care ◽

10.3233/thc-202744 ◽

2021 ◽

pp. 1-10

Author(s):

Shahul Mujib Kamal ◽

Norazryana Mat Dawi ◽

Hamidreza Namazi

Keyword(s):

Brain Activity ◽

Point Of View ◽

Physiological Signals ◽

Human Walking ◽

Eeg Signals ◽

Rehabilitation Science ◽

Information Contents ◽

First Time ◽

The Brain ◽

Brain Reaction

BACKGROUND: Walking like many other actions of a human is controlled by the brain through the nervous system. In fact, if a problem occurs in our brain, we cannot walk correctly. Therefore, the analysis of the coupling of brain activity and walking is very important especially in rehabilitation science. The complexity of movement paths is one of the factors that affect human walking. For instance, if we walk on a path that is more complex, our brain activity increases to adjust our movements. OBJECTIVE: This study for the first time analyzed the coupling of walking paths and brain reaction from the information point of view. METHODS: We analyzed the Shannon entropy for electroencephalography (EEG) signals versus the walking paths in order to relate their information contents. RESULTS: According to the results, walking on a path that contains more information causes more information in EEG signals. A strong correlation (p= 0.9999) was observed between the information contents of EEG signals and walking paths. Our method of analysis can also be used to investigate the relation among other physiological signals of a human and walking paths, which has great benefits in rehabilitation science.

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Neural Correlates of Mathematical Problem Solving

International Journal of Neural Systems ◽

10.1142/s0129065715500045 ◽

2015 ◽

Vol 25 (02) ◽

pp. 1550004 ◽

Cited By ~ 6

Author(s):

Chun-Ling Lin ◽

Melody Jung ◽

Ying Choon Wu ◽

Hsiao-Ching She ◽

Tzyy-Ping Jung

Keyword(s):

Problem Solving ◽

Spectral Power ◽

Mathematical Problem ◽

Neural Correlates ◽

Mathematical Problem Solving ◽

Single Digit ◽

Beta Power ◽

First Time ◽

Anterior Posterior ◽

Arithmetical Problem Solving

This study explores electroencephalography (EEG) brain dynamics associated with mathematical problem solving. EEG and solution latencies (SLs) were recorded as 11 neurologically healthy volunteers worked on intellectually challenging math puzzles that involved combining four single-digit numbers through basic arithmetic operators (addition, subtraction, division, multiplication) to create an arithmetic expression equaling 24. Estimates of EEG spectral power were computed in three frequency bands — θ (4–7 Hz), α (8–13 Hz) and β (14–30 Hz) — over a widely distributed montage of scalp electrode sites. The magnitude of power estimates was found to change in a linear fashion with SLs — that is, relative to a base of power spectrum, theta power increased with longer SLs, while alpha and beta power tended to decrease. Further, the topographic distribution of spectral fluctuations was characterized by more pronounced asymmetries along the left–right and anterior–posterior axes for solutions that involved a longer search phase. These findings reveal for the first time the topography and dynamics of EEG spectral activities important for sustained solution search during arithmetical problem solving.

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Impact of Visual Design Elements and Principles in Human Electroencephalogram Brain Activity Assessed with Spectral Methods and Convolutional Neural Networks

Sensors ◽

10.3390/s21144695 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4695

Author(s):

Francisco E. Cabrera ◽

Pablo Sánchez-Núñez ◽

Gustavo Vaccaro ◽

José Ignacio Peláez ◽

Javier Escudero

Keyword(s):

Brain Activity ◽

Music Videos ◽

Visual Design ◽

Music Video ◽

Eeg Signal ◽

Design Elements ◽

The Public ◽

Learning Classifier ◽

The Mean ◽

Human Electroencephalogram

The visual design elements and principles (VDEPs) can trigger behavioural changes and emotions in the viewer, but their effects on brain activity are not clearly understood. In this paper, we explore the relationships between brain activity and colour (cold/warm), light (dark/bright), movement (fast/slow), and balance (symmetrical/asymmetrical) VDEPs. We used the public DEAP dataset with the electroencephalogram signals of 32 participants recorded while watching music videos. The characteristic VDEPs for each second of the videos were manually tagged for by a team of two visual communication experts. Results show that variations in the light/value, rhythm/movement, and balance in the music video sequences produce a statistically significant effect over the mean absolute power of the Delta, Theta, Alpha, Beta, and Gamma EEG bands (p < 0.05). Furthermore, we trained a Convolutional Neural Network that successfully predicts the VDEP of a video fragment solely by the EEG signal of the viewer with an accuracy ranging from 0.7447 for Colour VDEP to 0.9685 for Movement VDEP. Our work shows evidence that VDEPs affect brain activity in a variety of distinguishable ways and that a deep learning classifier can infer visual VDEP properties of the videos from EEG activity.

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