Overview The Dream EEG and Mentation (DREAM) database collects and stores metadata about DREAM datasets, and is accessible to the public. DREAM datasets provide polysomnography and associated subjective mentation reports. Some datasets may also contain personally identifiable information about participants, but such information are not stored by the DREAM database. Datasets are contributed to DREAM from many different labs in many different studies and, where possible, made openly accessible in the hope of pushing the fields of sleep, dream, brain-computer interface, and consciousness research forward. If you have data that others in the community might find useful, please consider contributing it to DREAM. Contents The DREAM database consists of a following data tables:

Datasets Data records People

The records in Datasets list all officially accepted DREAM datasets and their summary metadata. Data records lists metadata of each individual datum from these datasets. People provides information on the data contributors, referred to by Key ID in Datasets.

========================================

Dream EEG and Mentation (DREAM) data set

Data set information

• Common name: Dream Database from Donders
• Full name: N/A
• Authors: Cagatay Demirel, Jarrod Gott, Martin Dresler
• Location: Donders Centre for Cognitive Neuroimaging
• Year: 2019 - 2021
• Set ID: 5
• Amendment: 0
• Corresponding author ID: 12

Previous publications:

Konkoly, K. R., Appel, K., Chabani, E., Mangiaruga, A., Gott, J., Mallett, R., ... & Paller, K. A. (2021). Real-time dialogue between experimenters and dreamers during REM sleep. Current Biology, 31(7), 1417-1427.

Correspondence:

cagatay.demirel@donders.ru.nl

Metadata

• Key ID: 6
• Date entered: 2023-02-21T12:48:54+00:00
• Number of samples: 7
• Number of subjects: 6
• Proportion REM: 14%
• Proportion N1: 14%
• Proportion N2: 43%
• Proportion W: 0%
• Proportion experience: 100%
• Proportion no-experience: 0%
• Proportion healthy: 100%
• Provoked awakening: Yes
• Time of awakening: Morning
• Form of response: Structured
• Date approved: 2023-02-21T12:47:51+00:00

How to decode data files

• EEG files: All the EEG data are already transformed into ".edf" format from original Brainvision files.
• Dream reports: Microsoft Word files in .docx format are used to format the dream reports in our study. Initially, we captured dream reports as .wav files and then meticulously refined the transcribed text to extract clear and accurate dream reports.

Treatment group codes

Codes correspond to the study numbers described below.

Experimental description

The database consists of data from three projects:

• Real-time dialogue during lucid dreaming (study no: 0): Subjects communicated with the researchers while lucid dreaming, and answered very basic calculations with eye signals (e.g "what is 8 minus 6? --> "answer is 2 by moving ocular muscles on both sides to count the amount of eye signalling).

• Motor-decoding during lucid dreaming (study no: 1): The goal is to induce hand-clenching during lucid dreams. In this experiment, participants are instructed to provide LRLR eye signals between each hand-clenching event to differentiate between the occurrences.

• Other dream data: No experimental description, just some high-density EEG data with the dream content (study no: 2).

Note: Please note that the majority of "dream recall" moments, clear phasic REM stages, and lucid dreaming (in some individuals) occurred around midday during our study. It is important to mention that all participants arrived at the EEG lab with high REM pressure around 7:00 a.m., and the events were observed between 11:00-12:00. As a result, our dream segments are more likely to be perceived as "day awakenings".

Note: The data was collected under blanket ethical approval, and informed consent for participation was obtained from subjects.

DREAM categorization procedure

N/A

Data acquisition

The equipment used in this study included: - Easycap 128-channel EEG device (using the 10/05 EEG layout) with passive electrodes, which included EMG, EOG, and ECG. - actiCAP 64-channel EEG device (using the 10/10 EEG layout). * During study no. 1 and no. 2, certain EEG channels are converted into EOG and skin EMG signals through the use of adhesive holders. To avoid confusion, all modified channels are given updated names to reflect their current function. * ExG box with additional passive and bipolar EOG and EMGs are utilized.

Data preprocessing

The EEG data in the .edf format has not been preprocessed and remains in its raw form. However, since the data was originally collected from Brainvision files, the 3D electrode layout information (using the 10/10 system) is already embedded in the .edf files. As a result, when loading the data into either FieldTrip or MNE platforms, the layout information will be automatically included, and there is no need to search for an external EEG layout to integrate the data structure into MATLAB or Python platforms.

Recommended data preprocessing for the whole data

Various preprocessing pipelines could be utilized for the intended analysis; however, this is the most general preprocessing pipeline identified for our specific dataset.

1) Channel type assignment 2) Notch filter at 50 Hz 3) 0.1 - 49 Hz band-pass filter (in case above 50 Hz are not interested). 4) Noisy-channel tagging & interpolation 5) Optionally, EEG channel interpolation can be performed to generate artificial EEG signals on channels that have been converted to EOG and EMG. 6) Signal-space projection (SSP): https://mne.tools/0.16/manual/preprocessing/ssp.html

The DREAMS Databases and Assessment algorithm

During the DREAMS project funded by Région Wallonne (Be), we collected a large amount of polysomnographic recordings (PSG) to tune, train and test our automatic detection algorithms.

These recordings were annotated in microevents or in sleep stages by several experts. They were acquired in a sleep laboratory of a belgium hospital using a digital 32-channel polygraph (BrainnetTM System of MEDATEC, Brussels, Belgium). The standard European Data Format (EDF) was used for storing.

In order to facilitate future research and performance comparision, we decided to publish these data on Internet. Therefore, eight DREAMS databases are available according to the annotation carried out (click on the link to open):

• The DREAMS Subjects Database: 20 whole-night PSG recordings coming from healthy subjects, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;

• The DREAMS Patients Database: 27 whole-night PSG recordings coming from patients with various pathologies, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;

• The DREAMS Artifacts Database: 20 excerpts of 15 minutes of PSG recordings annoted in artifacts (cardiac interference, slow ondulations, muscle artifacts, failing electrode, 50/60Hz main interference, saturations, abrupt transitions, EOG interferences and artifacts in EOG) by an expert;

• The DREAMS Sleep Spindles Database: 8 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in sleep spindles; PLEASE NOTICE THAT EXPERT 1's SCORED SPINDLE COUNTS WERE CUT OFF AFTER 1000 SECONDS. THIS MAKES IT DIFFICULT TO USE COUNTS FOR COMPARISON.

• The DREAMS K-complexes Database: 5 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in K-complexes;

• The DREAMS REMs Database: 9 excerpts of 30 minutes of PSG recordings in which rapid eye movements were annotated by an expert;

• The DREAMS PLMs Database: 10 whole-night PSG recordings coming from patients in which one of the two tibialis EMG was annoted in periodic limb movements by an expert;

• The DREAMS Apnea Database: 12 whole-night PSG recordings coming from patients annoted in respiratory events (central, obstructive and mixed apnea and hypopnea) by an expert.

We also developped and tested several automatic procedures to detect micro-events such as sleep spindles, K-complexes, REMS, etc. and provide the source codes for them in the DREAMS Assessment Algorithm package.

(MORE INFORMATION ON EACH DBA CAN BE FOUND in pdf file in this repository)

All our publications on this subject can be found in : https://www.researchgate.net/scientific-contributions/35338616_S_Devuyst

This repository duplicates the entire Sleep and Dream Database (SDDb), a public collection of dream reports. The purpose of this repository is (a) to provide a convenient access point for the dream reports, and (b) to provide a system of SDDb version control so that analysis of these dream reports can be replicated even when the official SDDb undergoes modifications. It contains all SDDb dream reports as of the date of download (see "Dates" section of this repository). No additional processing was applied.

This dataset mainly provides information on the Dream of the Round Picture Book Database, including the source of the collection, the title of the picture book, author, language, theme, and appropriate reading age, for public reference to the relevant information.

radar

Format is as follows:

dream_id: Incremental identifier of the dream
dreamer: short string identifier of the dreamer (from dreambank)
description: short description of the dreamer (from dreambank)
dream_date: approximate date of when the dream was reported; expressed in free-text, format may vary (from dreambank)
dream_language: language of dream
text_dream: the actual dream report, written by the dreamer
characters_code: Hall-Van de Castle (HVC) code that encode the characters present in the dream
emotions_code: HVC code that encode the emotions present in the dream
aggression_code: HVC code that encode the aggression interactions present in the dream
friendliness_code: HVC code that encode the friendlyinteractions present in the dream
sexuality_code: HVC code that encode the sexual interactions present in the dream
Male: %of male characters
Animal: %of animal characters
Friends: %of characters that are friends to the dre...

Dream EEG and Mentation (DREAM) data set ---Data set information--- Common name: Zhang & Wamsley 2019 Full name: N/A Authors: Jing Zhang, Erin Wamsley Location: Furman University, Greenville SC Year: N/A Set ID: [SET BY DATABASE] Amendment: [SET BY DATABASE] Corresponding author ID: [SET BY DATABASE] Download URL: [SET BY DATABASE] Previous publications: Zhang, J., & Wamsley, E. J. (2019). EEG predictors of dreaming outside of REM sleep. Psychophysiology, 56(7), e13368. Correspondence: Dr. Erin Wamsley: erin.wamsely@furman.edu ---Metadata--- Key ID: [SET BY DATABASE] Date entered: [SET BY DATABASE] Number of samples: [INFERRED BY DATABASE] Number of subjects: [INFERRED BY DATABASE] Proportion REM: [INFERRED BY DATABASE] Proportion N1: [INFERRED BY DATABASE] Proportion N2: [INFERRED BY DATABASE] Proportion experience: [INFERRED BY DATABASE] Proportion no-experience: [INFERRED BY DATABASE] Proportion healthy: [INFERRED BY DATABASE] Provoked awakening: Yes Time of awakening: Mixed Form of response: Free Date approved: [SET BY DATABASE] ---How to decode data files--- Sleep stage codes in the filenames/Case IDs: "SO#" = sleep onset reports collected after

This is a ZIP archive of a dataset package template for the Dream EEG and Mentation (DREAM) database. It embodies the minimal, basic directory structure and files required of a DREAM dataset package.

Version 0.5.0

DREAM database project page: https://bridges.monash.edu/projects/The_Dream_EEG_and_Mentation_DREAM_database/158987

Dream EEG and Mentation (DREAM) data set---Data set information---Common name: Zhang & Wamsley 2019Full name: N/AAuthors: Jing Zhang, Erin WamsleyLocation: Furman University, Greenville SCYear: N/ASet ID: [SET BY DATABASE]Amendment: [SET BY DATABASE]Corresponding author ID: [SET BY DATABASE]Download URL: [SET BY DATABASE]Previous publications:Zhang, J., & Wamsley, E. J. (2019). EEG predictors of dreaming outside of REM sleep. Psychophysiology, 56(7), e13368.Correspondence:Dr. Erin Wamsley: erin.wamsely@furman.edu---Metadata---Key ID: [SET BY DATABASE]Date entered: [SET BY DATABASE]Number of samples: [INFERRED BY DATABASE]Number of subjects: [INFERRED BY DATABASE]Proportion REM: [INFERRED BY DATABASE]Proportion N1: [INFERRED BY DATABASE]Proportion N2: [INFERRED BY DATABASE]Proportion experience: [INFERRED BY DATABASE]Proportion no-experience: [INFERRED BY DATABASE]Proportion healthy: [INFERRED BY DATABASE]Provoked awakening: YesTime of awakening: MixedForm of response: FreeDate approved: [SET BY DATABASE]---How to decode data files---Sleep stage codes in the filenames/Case IDs:"SO#" = sleep onset reports collected after

This dataset comprise behavioural data recorded from 61 children diagnosed with Autism Spectrum Disorders (ASD). The data was collected during a large-scale evaluation of Robot Enhanced Therapy (RET). The dataset covers over 3000 therapy sessions and more than 300 hours of therapy. Half of the children interacted with the social robot NAO supervised by a therapist. The other half, constituting a control group, interacted directly with a therapist. Both groups followed the Applied Behavior Analysis (ABA) protocol. Each session was recorded with three RGB cameras and two RGBD (Kinect) cameras, providing detailed information of children's behaviour during therapy. This public release of the dataset noes not include video recordings or other personal information. Instead, it comprises body motion, head position and orientation, and eye gaze variables, all specified as 3D data in a joint frame of reference. In addition, metadata including participant age, gender, and autism diagnosis (ADOS) variables are included.

All data in this dataset is stored in JavaScript Object Notation (JSON) and can be downloaded here as DREAMdataset.zip. A much smaller archive comprising example data recorded from a single session is provided in DREAMdata-example.zip. The JSON format is specified in detail by the JSON Schema (dream.1.1.json) provided with this dataset.

JSON data can be read using standard libraries in most programming languages. Basic instructions on how to load and plot the data using Python and Jupyter are available in DREAMdata-documentation.zip attached with this dataset. Please refer to https://github.com/dream2020/data for more details.

The DREAM Dataset can be visualized using the DREAM Data Visualizer, an open source software available at https://github.com/dream2020/DREAM-data-visualizer. The DREAM RET System that was used for collecting this dataset is available at https://github.com/dream2020/DREAM.

Dream EEG and Mentation (DREAM) data set

Data set information

• Common name: TWC_USA
• Full name: Two-way communicaton data from USA team
• Authors: Karen R. Konkoly, Remington Mallett, Ken A. Paller
• Location: Northwestern University
• Year: 2021
• Set ID: 4
• Amendment: 1
• Corresponding author ID: 4

Previous publications:
Konkoly, K. R., Appel, K., Chabani, E., Mangiaruga, A., Gott, J., Mallett, R., ... & Paller, K. A. (2021). Real-time dialogue between experimenters and dreamers during REM sleep. Current Biology, 31(7), 1417-1427.

Correspondence:
karenkonkoly2023@u.northwestern.edu

Metadata

• Key ID: 5
• Date entered: 2023-02-08T03:09:10+00:00
• Number of samples: 33
• Number of subjects: 19
• Proportion REM: 61%
• Proportion N1: 18%
• Proportion N2: 18%
• Proportion W: 0%
• Proportion experience: 82%
• Proportion no-experience: 15%
• Proportion healthy: 100%
• Provoked awakening: Some
• Time of awakening: Mixed
• Form of response: Structured
• Date approved: 2023-02-09T05:50:12+00:00

How to decode data files

• L-MSTD is an electrode on the left mastoid for if a back-up reference channel was needed. The EMG channel is on the chin, and channels 26 and 27 are back-up EMG electrodes located nearby on the chin. On some recordings EMG-2 is the back-up EMG channel instead, which is also located nearby on the chin.
• The "status" channel was created when converting the data to EDF, and contains information about port codes in the data set. Disregard that the port codes are expressed in microvolts. More information about the meanings of the port codes below.
• It may be that there is a second of flat EEG at the end of each recording which appears to be an artifact of converting the file type to .EDF and should be disregarded

• The time of awakening column contains only approximated times based on experimenters' notes and the duration of files

• There are port codes in the data that have slightly meanings for some different participants (in the "status" channel). Here is a guide for their meanings:

  • 32 just indicates that a new script was started (no sounds played)
  • 64 means the volume was turned down (no sounds played)
  • 65 means the volume was turned up (no sounds played)
  • 29 means a TLR auditory cue was presented
  • 23 means a TLR light cue was presented (but code and light cue are triggered manually, so time-locked analyses is not possible here)
  • Codes 1-20 correspond to math problems that were presented during sleep, and were changed after case 08. See below for guide

  Cases 01-08

  • 1: 9-7
  • 2: 3+2
  • 3: 14-13
  • 4: 6+1
  • 5: 19-16
  • 6: 1+1
  • 7: 5-2
  • 8: 1+4
  • 9: 15-10
  • 10: 3+3
  • 11: 8-4
  • 12: 2+2
  • 13: 8-0
  • 14: 4+1
  • 15: 14-13
  • 16: 2+4
  • 17: 16-13
  • 18: 3+1
  • 19: 10-8
  • 20: 1+0

  Cases 09-33

  • 1: 9-7
  • 2: 3+1
  • 3: 8-7
  • 4: 1+2
  • 5: 9-6
  • 6: 1+1
  • 7: 5-2
  • 8: 4-1
  • 9: 8-6
  • 10: 8-5
  • 11: 2+2
  • 12: 2+1
  • 13: 3+0
  • 14: 1+0
  • 15: 7-4
  • 16: 2+0
  • 17: 6-3
  • 18: 3-1
  • 19: 5-4
  • 20: 1+0

Treatment group codes

N/A

Experimental description

Methods:

Twenty-two participants (15 female, age range 18-33 years, M = 21.1 ± 4.3 years) who claimed to remember at least one dream per week were recruited by word of mouth, online forum, and the Northwestern University Psychology Department participant pool. They each participated in one or more nap sessions, which amounted to 27 nap sessions in total.

Procedure:

Participants visited the laboratory at Northwestern University at approximately their normal wake time and received guidance on identifying lucid dreams and instructions for the experiment for about 40 min during preparations for polysomnographic recordings, including EEG, EMG, and EOG, using a Neuroscan SynAmps system. Participants were instructed to signal with a prearranged number of LR eye movements if they became lucid in a dream.

Next, participants practiced making ocular signals and responding to questions using combinations of LR signals. Subsequently, participants completed the Targeted Lucidity Reactivation (TLR) procedure while lying in bed. This procedure was derived from the procedure developed by Carr and collegues. A method of reality checking to induce lucid dreaming was paired with sensory stimulation and accelerated in a single session immediately before sleep, and then cues were presented again during REM sleep. In this procedure, participants were trained to associate a novel cue sound with a lucid state of mind during wake. The sound consisted of three pure-tone beeps increasing in pitch (400, 600, and 800 Hz) at approximately 40-45 dB SPL and lasting approximately 650 ms. For one participant (ppt. 121) the pure-tone beeps had previously been associated with a different task in an unrelated study. Thus, for this participant, a 1000-ms violin sound and low-intensity flashing-red LED lights were used as cues. All participants were informed that this cue would be given during sleep to help promote a lucid dream. Over the next 15 min, the TLR sound was played up to 15 times. The first 4 times, it was followed by verbal guidance to enter a lucid state as follows. ‘‘As you notice the signal, you become lucid. Bring your attention to your thoughts and notice where your mind has wandered.[pause] Now observe your body, sensations, and feelings.[pause] Observe your breathing. [pause] Remain lucid, critically aware, and notice how aspects of this experience are in any way different from your normal waking experience.’’

Participants often fell asleep before all 15 TLR cue presentations were completed. Standard polysomnographic methods were used to determine sleep state. Once participants entered REM sleep, TLR cues were presented again, at about 30-s intervals, as long as REM sleep remained stable. After participants responded to a cue with a lucid eye signal, or after approximately 10 cues were presented without response, we began the math problem portion of the experiment.

We devised the following task to engage auditory perception of math problems, working memory, and the ability to express the correct answer. We used simple addition and subtraction problems that could each be answered by a number between 1 and 4 (LR = 1, LRLR = 2, LRLRLR = 3, LRLRLRLR = 4), or between 1 and 6 for the first 5 participants.

From the above dataset, data was included in DREAM if there was a period of sleep on the EEG followed by a report of a dream (or a lack of dream). The EEG data includes the last period of continuous sleep before the dream report was collected, starting with the first epoch scored as wake, and ending at the last second before clear movement/alpha activity indicating wake. Also, there are a few instances, noted in the “Remarks” column in the “Records” file, where I included epochs that were scored as wake, when the wake seemed due to alpha from participants attempting to answer questions with eye movements (only one of these included wake in the last 20 seconds of the recording, case21_sub111).

EEG sleep data was NOT included if it was not followed by a verbal/written dream report or clear note on the experimenter’s log that there was no recall. Also not included is data where participants completed eye signals or answered questions, but it was not part of the continuous period of sleep before a dream report was given. Also excluded was a case in which a dream report was collected at the end of the nap but the participant had been in and out of sleep beforehand, so it was unclear which sleep period the report referred to.

DREAM categorization procedure

Karen Konkoly rated reports according to the DREAM categorization. If the participant reported remembering any sort of mental content from sleep, it was rated “2”. If the participant reported remembering a dream but none of its content, it was rated “1”. If the participant reported not remembering anything, or not falling asleep, it was rated “0”.

Dream EEG and Mentation (DREAM) dataset Dataset information

Common name: Tononi Serial Awakenings Full name: N/A Authors: Francesca Siclari, Benjamin Baird, Lampros Perogamvros, Melanie Boly, Joshua LaRocque, Giulio Tononi Location: Madison, WI Year: 2016 Set ID: 13 Amendment: 1 Corresponding author ID: 17

Previous publications:

Siclari et al Nature Neuroscience 2017

Correspondence:

Lampros Perogamvros (email: lampros.perogamvros@unige.ch)

Metadata

Key ID: 18 Date entered: 2023-07-18T09:17:08+00:00 Number of samples: 261 Number of subjects: 36 Proportion REM: 0% Proportion N1: 0% Proportion N2: 100% Proportion W: 0% Proportion experience: 42% Proportion no-experience: 39% Proportion healthy: 100% Provoked awakening: Yes Time of awakening: Night Form of response: Free Date approved: 2023-07-18T09:26:37+00:00

How to decode data files File "Data/HydroCelGSN256v10.sfp" gives the channel locations for all PSGs. It is a BESA surface point coordinate or EGI-xyz Cartesian coordinate file (.sfp); details about this format may be found at http://wiki.besa.de/index.php?title=Working_With_Additional_Files&oldid=5145. Treatment group codes N/A Experimental description Procedure. The procedure used in this study has been described in detail in a previous publication9. Awakenings in the sleep laboratory were performed at pseudorandom intervals, irrespective of sleep stage, using a computerized sound that lasted 1.5 s, which was administered through E-Prime (Psychology Software Tools, Pittsburgh, PA). Subjects were instructed to signal that they had heard the alarm sound and to lie quietly on their back. They then underwent a structured interview via intercom about their mental activity that lasted between 20 s and 3.5 min, depending on whether the subject reported a conscious experience and had to answer additional questions related to the content. Signed informed consent was obtained from all participants before the experiment, and ethical approval for the study was obtained from the University of Wisconsin–Madison Institutional Review Board. Data collection and analysis were not performed blind to the conditions of the experiments. Study participants in experiment 1. Thirty-two healthy subjects (12 males, age 46 ± 13.3 years (mean ± s.d.), range 24–65 years), selected from a group of 69 subjects participating in a larger research project in our laboratory, were included in this experiment. Subjects were randomly selected from the subset of all participants that had at least one DE and NE report in NREM sleep during the same night. Among the 240 awakenings performed in these 32 subjects, 5 had to be excluded for technical problems and 2 because subjects were too somnolent upon awakening to answer questions reliably. Comparisons between DEWR–NE conditions and DE–DEWR conditions were performed in the subset of these participants (N = 20) that had all three types (DE, DEWR, NE) of report during the same night. In REM sleep, only 6 of the 69 participants presented both DE and NE. They were all included in the analysis. Sleep recordings. Recordings were made at the Wisconsin Center for Sleep and Consciousness. They were performed using a 256-channel high-density EEG (hd-EEG) system (Electrical Geodesics, Inc., Eugene, Ore.). Four of the 256 electrodes placed at the outer canthi of the eyes were used to monitor eye movements; additional polysomnography channels were used to record submental electromyography. Sleep scoring was performed over 30-s epochs, according to standard criteria. DREAM categorization procedure We have converted dream categories in Records.csv as DE = Experience, DEWR = Experience without recall, NE = No Experience. Technical details N/A Data acquisition EGI Geodesic with concurrent Alice PSG Data preprocessing Raw data (no preprocessing)

Presented is an implementation of the SALAD algorithm for dream content analysis through word searching. Helper functions for constructing initial seed word dictionaries are provided in "hyponym_dictionary.py" which will also be used to construct the dictionaries from the seed words. "read_csv.py" reads and pre-processes the dream reports into a dictionary that captures the linguistic features of the words and sentences from the dreams. It also contains an implementation of the Improved Lesk Algorithm. The folder Series/ can be populated with data from any dream journal (you can take data from www.dreambank.net). The required data format is a csv file containing one dream in each row. The code "search_lemmas.py" performs the actual word search. The exact steps of SALAD and the parameters that need to be played around with to obtain the best results are described in the paper. The codes are written in Python 3.6 and can run on Python 3.6 and above.

Comprehensive dataset containing 12 verified Dream locations in United States with complete contact information, ratings, reviews, and location data.

Dream EEG and Mentation (DREAM) data set

Please see ExperimentalDescription.txt for a full description

Most psychologists are likely to have at least some clients bring a dream into therapy. In the few studies looking at the use of dreams in therapy, therapists report that they do not feel confident or competent to adequately respond to their clients' introduction of dream material in therapy. The possible consequences of this include a negative impact on the therapeutic alliance and misinterpretation of the therapist's rejection of a dream narrative as a disinterest in the client's inner life. This research project seeks to identify psychologists' and psychology clients' understanding of their experiences of the use of dream material in therapy and their understanding of the role of dreams in contemporary psychological practice. While there have been some surveys about the use of dreams in therapy, relatively little is known about this topic, so a phenomenological, qualitative approach will be used. This research will be broken into two studies. The first study will use semi-structured interviews to interview psychologists and the second study will use semi-structured interviews to interview psychology clients. A hermeneutic phenomenological analysis of the interview transcripts will be completed with the aid of Dedoose software.

Dream EEG and Mentation (DREAM) data set

---Data set information---

Common name: Aamodt_evening_sleep Full name: (Optional, unwieldly full name) Authors: Aamodt, A., Sevenius Nilsen, A., Markhus, R., Kusztor, A., HasanzadehMoghadam, F., Kauppi, N., Thürer, B., Storm, J.F. and Juel, B.E. Location: Oslo, Norway Year: 2022 Set ID: [SET BY DATABASE] Amendment: [SET BY DATABASE] Corresponding author ID: [SET BY DATABASE] Download URL: [SET BY DATABASE]

Previous publications: 1) Aamodt, A., Nilsen, A.S., Thürer, B., Moghadam, F.H., Kauppi, N., Juel, B.E. and Storm, J.F. (2021) ‘EEG Signal Diversity Varies With Sleep Stage and Aspects of Dream Experience’, Frontiers in psychology, 12, p. 655884. 2) Aamodt, A., Sevenius Nilsen, A., Markhus, R., Kusztor, A., HasanzadehMoghadam, F., Kauppi, N., Thürer, B., Storm, J.F. and Juel, B.E. (2023) ‘EEG Lempel-Ziv complexity varies with sleep stage, but does not seem to track dream experience’, Frontiers in human neuroscience, 16. Available at:

Correspondence: Bjørn E Juel (b.e.juel@medisin.uio.no)

---Metadata---

Key ID: [SET BY DATABASE] Date entered: [SET BY DATABASE] Number of samples: [INFERRED BY DATABASE] Number of subjects: [INFERRED BY DATABASE] Proportion REM: [INFERRED BY DATABASE] Proportion N1: [INFERRED BY DATABASE] Proportion N2: [INFERRED BY DATABASE] Proportion experience: [INFERRED BY DATABASE] Proportion no-experience: [INFERRED BY DATABASE] Proportion healthy: [INFERRED BY DATABASE] Provoked awakening: (Whether the study protocol instated provoked awakenings; choose Yes, No, or Some) Time of awakening: (General time of day when the awakenings occurred; choose Morning, Day, Evening, Night, or Mixed) Form of response: (The form of response given by subjects, used to infer their experience; choose Free, Structured, Categorical, or Other) Date approved: [SET BY DATABASE]

---How to decode data files---

The files in the PSG directory are named according to the subjectID and the CaseNumber for each awakening. For example the file 10_0103.edf is associated with the third awakening (case 0103) from subject 10.

--Treatment group codes--

No treatment groups

---Experimental description---

See previous publications

--DREAM categorization procedure--

The original experiment categorized the dream reports according to the DREAM definitions.

---Technical details---

The data include a single EMG channel (called EMG1), which is a bipolar derivation of the standard LAT and RAT electrodes placed on the chin of the sleeping subject. The data include two EOG channels (called EOGu and EOGl), which were placed in the American Academy of Sleep Medicine (AASM) recommended E1 and E2 positions below (l for lower) and above (u for upper) the lateral canthi.

--Data acquisition--

See published manuscripts

--Data preprocessing--

Data were rereferenced to a common average reference and zero centered to adjust for the DC offset. Raw data can be requested in accordance with statements in published manuscripts.

Comprehensive dataset containing 1 verified Dream World locations in United States with complete contact information, ratings, reviews, and location data.

Sleep is known to play a role in making memories permanent. This project investigates how such consolidation of memories occurs during sleep. In 4 studies participants will keep a daily diary and will also produce reports of their dreams, either at home or in the sleep laboratory. How dream content is related to events from waking life will be studied. Incorporations of waking life events into dreams can be literal replications of events, indirect representations, or even metaphors.The timescale of incorporations will be examined, aiming to replicate the dream-lag effect, in which events from 5-7 days before the dream are incorporated as frequently as events from 1-2 days before the dream, with a dip in incorporations on days 3-4. This effect may indicate an approximately week-long memory processing function for sleep. Because Rapid Eye Movement Sleep (REM) is thought to be especially important for the consolidation of emotional memories, the project differentiates REM dreams from non-REM dreams, and emotional from neutral memories.The project also tests whether specific experimenter induced experiences are incorporated into dreams in a delayed manner, and uses a direct behavioural test of how memory consolidation occurs across several periods of sleep. 14 day daily log and dream diaries were kept by participants. After the 14 days participants identified any similarities between any part of each dream and the daily logs. All daily logs were compared to all dream diaries. The daily logs recoded daily events under 3 categories: major activities, personally important events, and major concerns. The SPSS database relates to these 3 categories of waking life events, recording for ecah category the number of incorportations per dream of that category as a function of number of days between the evebt and the dream.