Discover datasets

This dataset comprises neuroimaging data from a logical reasoning study conducted by the Cognitive and Computational Neuroscience Laboratory. The study investigates neural correlates of logical reasoning processes through multimodal brain imaging. Data were collected and organized according to BIDS standards for reproducibility and accessibility. This dataset is a NEMAR mirror of the OpenNeuro dataset ds003483.

Participants

21

Size

68.3 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises resting-state EEG recordings from 122 college-age participants with varying levels of depressive symptoms as measured by the Beck Depression Inventory. Participants completed two 6-minute resting sessions with eyes-open and eyes-closed conditions, with the first session occurring immediately after EEG setup and the second following approximately one hour of task performance. The data were collected between 2008-2010 in the John J.B. Allen lab at the University of Arizona and include clinical interview information for a subset of participants. NOTE: Data quality considerations include potential HEOG/VEOG mislabeling for some subjects, some channels with prior interpolation, and limited review of the second rest run. Subject 516 is missing rest2 data, and subject 544 was excluded from analyses due to unstable BDI scores between assessment timepoints.

Participants

122

Channels

66 (10-10)

Size

10.6 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 121 college-age participants (122 collected, 1 excluded) performing a probabilistic selection task, stratified by depression symptomatology as measured by the Beck Depression Inventory. Collected between 2008-2010 in the lab of John J.B. Allen at the University of Arizona, the data were acquired to investigate neural correlates of decision-making and reward processing in relation to depressive symptoms. Some participants underwent clinical interviews to confirm depression status. NOTE: Data quality considerations include potential mislabeling of HEOG/VEOG channels in some subjects, and some files have had channels interpolated already with no raw data available for reversion. Subject 544 was excluded due to unstable BDI scores between pre-assessment and test session.

Participants

122

Channels

66 (10-10)

Size

16.6 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises electroencephalography (EEG) recordings from 23 healthy college students performing a three-armed bandit gambling task with dynamically oscillating reward probabilities. The task was designed to investigate neural correlates of reward processing and reward prediction error during adaptive decision-making and task set generation. Each stimulus condition (blue, red, and green) had oscillating reinforcement probabilities (ranging from 20% to 90%) that moved in different phases across trials. Supplementary physiological recordings including corrugator EMG and skin conductance were acquired on most participants; however, data quality for these supplementary recordings was limited and they were not extensively analyzed. Data were collected in 2014 at the University of New Mexico's Cognitive Rhythms and Computation Lab.

Participants

23

Channels

64 (10-10)

Size

4.72 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This high-density EEG dataset comprises recordings from 20 healthy right-handed volunteers (10 females, 10 males, mean age 23 years) collected between 2014 and 2017 during four experimental conditions: resting state, visual naming, auditory naming, and working memory tasks. The dataset contains 256-channel EEG data acquired at 1000 Hz sampling frequency and is designed to investigate dynamic functional brain network reorganization across different cognitive states.

Participants

20

Channels

257 (egi-geodesic)

Size

76.8 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This high-density EEG dataset comprises resting-state and task-based recordings from 23 healthy volunteers performing visual naming and spelling tasks. Collected between 2012-2013 at Rennes University Hospital using a 256-electrode EEG system, the dataset enables investigation of dynamic functional brain network reorganization during language production and cognitive processing. The study provides insights into spatiotemporal patterns of brain connectivity during object naming and orthographic tasks.

Participants

23

Channels

256 (egi-geodesic)

Size

90.4 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This magnetoencephalography (MEG) dataset comprises recordings from 10 participants performing a human visual motion area (hMT+) localizer task involving passive viewing of coherent and incoherent moving dot stimuli. The dataset includes concurrent anatomical MRI scans and auxiliary physiological recordings (EOG, ECG), formatted according to MEG-BIDS standards. The data were acquired to investigate visual perceptual learning and motion perception mechanisms.

Participants

12

Size

10.1 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises intracranial EEG recordings and single-unit neuronal activity from the human amygdala of nine epilepsy patients during exposure to dynamic visual stimuli with aversive (fearful faces) and neutral (landscape) content. The recordings enable investigation of amygdalar responses to emotional stimuli across multiple spatial scales, from macroscopic field potentials to microscopic neuronal firing patterns. Data are provided in BIDS format with extended neuronal spike data available in NIX standard.

Participants

9

Size

297 MB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset contains intracranial electrophysiological recordings acquired during visual stimulus presentation. Participants were exposed to eight spiral grating stimuli of varying colors and luminance levels, as well as color word stimuli, with each stimulus presented for 116 ms. The dataset includes event markers corresponding to each stimulus type, enabling investigation of neural responses to visual and linguistic stimuli.

Participants

18

Size

214 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset contains somatosensory MEG recordings converted to BIDS format, including raw MEG data, anatomical MRI scans, and FreeSurfer-derived structural derivatives. The dataset includes forward models and boundary element models computed using MNE-BIDS, providing a complete neuroimaging resource for studying somatosensory processing and demonstrating MEG-MRI integration workflows.

Participants

1

Size

371 MB

Version

vv1.0.0

Updated

Jul 10, 2026