Showing 10 of 613 datasets · page 47 of 62
This dataset was curated for publication as part of the manuscript in Sakakura et al. (in preparation). It contains iEEGs collected from 114 individuals during slow wave sleep. The available Matlab code can be found at https://github.com/kaz1126/MI_HFO. The iEEG coordinate system employed in this dataset is MNI305.
References ---------- Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., D
Probabilistic Selection Task. Unpublished! Same sample as this published study: 10.1038/ncomms6394. Study 1: 80 healthy participants + 5 placebo session from a pilot of the drug study. Total n=85. But Subj 173 might have bad EEG. Study 2: 30 healthy participants (3 dropout) in a double-blind drug study. Total n=27. Drug was Cabergoline 1.25 mg. If you look in the code folder at the .xls sheet, you'll see that subjects had different initial IDs. Study 1 subjects had subje
Preprocessed data from Experiment 1 of Ester & Pytel "Changes in behavioral priority influence the accessibility of working memory content". Analytic scripts for this project can be found on OSF: https://osf.io/gtd5f/. Note that to analyze the BIDS data, you'll need to modify the analysis scripts to read in the BIDS .set files rather than the expected .mat files. See the OSF wiki for more information
Preprocessed data from Experiment 2 of Ester & Pytel "Changes in behavioral priority influence the accessibility of working memory content". Analytic scripts for this project can be found on OSF: https://osf.io/gtd5f/. Note that to analyze the BIDS data, you'll need to modify the analysis scripts to read in the BIDS .set files rather than the expected .mat files. See the OSF wiki for more information
Preprocessed data files from "Internal selective attention is delayed by competition between endogenous and exogenous factors". A preprint describing the work can be found at https://www.biorxiv.org/content/10.1101/2022.07.05.498906v4.abstract, and analysis scripts can be found at https://osf.io/wat6d/. This study was conceptualized and analyzed before our lab made the switch to BIDS archival. If you want to use the analysis scripts linked above to analyze the BIDS data, you'll have to modify th
### Description This dataset contains electroencephalography (EEG) signals recorded from 7 participants while performing an auditory imagery task. Participants were asked to imagine the sounds made by an object for 5 seconds. ### EEG EEG data were acquired with a BioSemi ActiveTwo system with 64 electrodes positioned according to the international 10-20 system, plus one electrode on each earlobe as references ('EXG1' channel is the left ear electrode and 'EXG2' channel is the right ear electro
Affective state reinforcement learning task in N=54 Community participants. High and low drinkers. Data collected from 2019-2021 in the CRCL at UNM. The paper [Singh, G., Campbell, E., Hogeveen, J; Witkiewitz,K., Claus, E.D., & Cavanagh, J.F. Alcohol Imagery Boosts The Reward Positivity in Heavy Drinkers] Under review at the moment. Your best bet for understanding this task would be to read that paper first. - James F Cavanagh 08/02/2022
### Description This dataset contains simultaneous electroencephalography (EEG) and near-infrared spectroscopy (fNIRS) signals recorded from 12 participants while performing a silent naming task and three sensory-based imagery tasks using visual, auditory, and tactile perception. Participants were asked to visualize an object in their minds, imagine the sounds made by the object, and imagine the feeling of touching the object. ### EEG EEG data were acquired with a BioSemi ActiveTwo system with
# Overview This dataset was collected in 2020 and comprises electroencephalography, physiological and behavioural data. The dataset includes both resting-state (eyes closed) and task-related neurophysiological signals acquired from 44 healthy individuals (ages: 21-40). The tasks administered to subjects include a spontaneous deception task (Gambling Game; GG) as well as a task assessing cognitive control (CC). # Task Description ## Spontaneous Deception Task (GG) Participants were informed th