on004809
Categorized Free Recall: Delayed Free Recall of Word Lists Organized by Semantic Categories
BEH iEEG
[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on004809-blue…
- Citations
- 1
- Size
- 477 GB
- Updated
- Jun 25, 2026
Discover datasets
73 results for "subcortical encoding" · page 5 of 8 · ranked by relevance
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- on002814
A Multimodal Neuroimaging Dataset to Study Spatiotemporal Dynamics of Visual Processing in Humans
ANAT EEG FUNCA multimodal neuroimaging dataset designed to investigate the spatiotemporal dynamics of visual processing in humans. The dataset combines multiple neuroimaging modalities—electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and structural MRI—to characterize neural responses during visual tasks. This comprehensive resource provides simultaneous EEG-fMRI recordings that enable investigation of the temporal and spatial organization of visual cortical processing, bridging the high temporal resolution of EEG with the high spatial resolution of fMRI.
- Size
- 48.6 GB
- Updated
- Jun 21, 2026
- on003505
VEPCON: Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes
ANAT DWI EEGVEPCON is a multimodal neuroimaging dataset comprising high-density EEG, structural MRI, and diffusion-weighted imaging from 20 participants performing visual discrimination tasks. The dataset includes raw data, preprocessed EEG single trials, individual brain parcellations at multiple spatial scales (83-1015 regions), structural connectomes derived from diffusion imaging, and EEG source imaging solutions. This resource supports multimodal methods development, structure-function relationship studies, and optimization of source imaging and graph analysis techniques.
- Citations
- 20
- Size
- 56.7 GB
- Updated
- Jun 22, 2026
- nm000232
THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition
BEH EEG…https://github.com/gifale95/eeg_encoding OSF: https://osf.io/3jk45/
- Size
- 159 GB
- Updated
- May 16, 2026
- on004785
EEG data for paper titled - Precise cortical contributions to feedback sensorimotor control during reactive balance
EEGThis dataset contains electroencephalography (EEG) recordings collected to investigate the precise cortical contributions to feedback sensorimotor control during reactive balance tasks. The data supports research examining how the brain processes sensory feedback and generates motor commands to maintain postural stability in response to perturbations.
- Citations
- 1
- Size
- 351 MB
- Updated
- Jun 25, 2026
- on005530
Depotentiation of emotional reactivity using TMR during REM sleep
ANAT BEH EEG FMAP FUNC…pulse oximetry 48 hours after encoding, and they completed an online follow…
- Size
- 6.47 GB
- Updated
- Jun 27, 2026
- on005522
Spatial Navigation Memory of Object Locations
BEH iEEG…The experiment consists of participants encoding object locations during a guided navigation…
- Size
- 106 GB
- Updated
- Jun 27, 2026
- on005273
Neural representation of consciously seen and unseen information
EEGThis dataset contains neural recordings investigating the brain's representation of consciously perceived versus unconsciously processed visual information. The study examines how neural activity differs when identical visual stimuli are either consciously seen or remain outside conscious awareness, providing insights into the neural correlates of consciousness and visual perception.
- Citations
- 1
- Size
- 44.4 GB
- Updated
- Jun 26, 2026
- on003702
Social Memory cuing
EEG[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on003702-blue…
- Citations
- 3
- Size
- 60.9 GB
- Updated
- Jun 22, 2026
- nm000259
Speier et al. 2017 — A comparison of stimulus types in online classification of the P300 speller using language models
EEGThis dataset comprises EEG recordings from 10 healthy participants performing a P300 speller task using a 6×6 character matrix. The study compares two stimulus conditions—famous faces and inverting—to evaluate their effects on online P300 classification performance using language models. Data were collected across 2 sessions per subject with 3 runs per session, sampled at 256 Hz from 32 EEG channels.
- Citations
- 28
- Size
- 288 MB
- Updated
- Jun 12, 2026