A repeated awakening study exploring the capacity of complexity measures to capture dreaming during propofol sedation
- Participants
- 21
- Size
- 77.3 GB
- Updated
- Jun 6, 2026
Showing 10 of 530 datasets · page 22 of 53
Generated from raw data by MNE-BIDS (Appelhoff et al., 2019) and custom code to join to behavioural data, stimulus information, and metadata. ## Notes on the Data For full details on this dataset, see our preprint: Taylor et al. (2024) https://doi.org/10.1101/2024.11.11.622929 General notes: * An issue during recording meant that sub-05 completed the first block without data being saved. The experiment was restarted from the beginning for this participant. This participant was not included i
**Passing Viewing Task** 23 participants took part in this study in return for a monetary incentive at University of Marburg. Participants performed a passive viewing task in a dimly lit room. The visual scene consisted of a game board, game pieces and a mesh as an occluder. Each trial started with a fixation cross presentation for one second plus the duration of the drift correction procedure. The game board and occluder were presented for two seconds, while game pieces only appeared
The "Podcast" ECoG dataset for modeling neural activity during natural story listening. We introduce the “Podcast” electrocorticography (ECoG) dataset for modeling neural activity supporting natural narrative comprehension. This dataset combines the exceptional spatiotemporal resolution of human intracranial electrophysiology with a naturalistic experimental paradigm for language comprehension. In addition to the raw data, we provide a minimally preprocessed version in the high-gamma spectral b
Data collection took place at the NeuroCognition Laboratory (NCL) in San Diego, California under the supervision of Dr. Phillip Holcomb. This project followed the San Diego State University’s IRB guidelines. Participants sat in a comfortable chair in a darkened sound attenuated room throughout the experiment. They were given a gamepad for button pressing. They were instructed to watch the LCD video monitor that was at a viewing distance of 150cm. Participants were presented with 300 prime-ta
### Categorized Free Recall with Closed-Loop Stimulation at Encoding (Encoding Classifier) #### Description This dataset contains behavioral events and intracranial electrophysiology recordings from a categorized free recall task with closed-loop stimulation at encoding, using a classifier trained on encoding data. The experiment consists of participants studying a list of words, presented visually one at a time, completing simple arithmetic problems that function as a distractor, and then fre
### Free Recall with Closed-Loop Stimulation at Encoding (Encoding Classifier) #### Description This dataset contains behavioral events and intracranial electrophysiology recordings from a delayed free recall task with closed-loop stimulation at encoding, using a classifier trained on encoding data. The experiment consists of participants studying a list of words, presented visually one at a time, completing simple arithmetic problems that function as a distractor, and then freely recalling th
# README The **Bitbrain Open Access Sleep (BOAS)** dataset. ## Overview This project aimed at bridging the gap between gold-standard clinical sleep monitoring and emerging wearable EEG technologies. The dataset contains data from **128 nights** in which participants were simultaneously monitored with two technologies: a **Brain Quick Plus Evolution PSG system by Micromed** and a **wearable EEG headband by Bitbrain**. The Micromed PSG system records a comprehensive and clinically validated set
This dataset was curated for publication as part of the manuscript in Kanno et al. (in preparation). It contains iEEGs collected from 106 individuals during auditory naming task. The available Matlab code can be found at https://github.com/a8k8nn0/TractographyAtlas. The iEEG coordinate system employed in this dataset is MNI305.