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The Natural Object Dataset (NOD) integrates electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) data collected from the same subjects viewing naturalistic object stimuli from ImageNet. This multimodal neuroimaging dataset enables investigation of neural mechanisms underlying object recognition in natural scenes by combining high spatial resolution (fMRI) with high temporal resolution (EEG/MEG) recordings, addressing limitations of single-modality approaches in characterizing both spatial representation patterns and temporal dynamics of visual cognitive processing.

Citations

5

Size

24.3 GB

Updated

Jun 28, 2026

This dataset comprises simultaneous 64-channel EEG and 3T fMRI recordings from 16 subjects performing motor imagery and neurofeedback tasks. Participants were randomly assigned to receive either mono-dimensional or bi-dimensional neurofeedback displays during five experimental runs with alternating rest and task blocks. The dataset includes raw EEG data in Brain Vision format, preprocessed EEG data, BOLD fMRI acquisitions, computed neurofeedback scores from both modalities (EEG and fMRI), and event timing files, providing a comprehensive resource for multimodal neuroimaging data integration studies.

Size

25.9 GB

Updated

Jun 21, 2026

Citations

2

Size

17.5 GB

Updated

May 18, 2026

This dataset comprises simultaneous EEG and fMRI recordings from 10 subjects performing motor imagery and neurofeedback tasks. Participants completed six runs including motor localization, pre- and post-neurofeedback motor imagery, and three neurofeedback conditions (bimodal EEG-fMRI, unimodal EEG, and unimodal fMRI). The dataset provides both raw and preprocessed EEG data (64 channels at 5 kHz), structural and functional MRI data (3T Siemens, 2×2×4 mm³ resolution), and computed neurofeedback scores, enabling multi-modal neuroimaging data integration studies.

Size

18.0 GB

Updated

Jun 21, 2026

This event-related potential (ERP) study investigates semantic and syntactic language processing in deaf readers by examining neural responses to grammatical violations and semantic anomalies. Participants viewed 180 sentences presented in rapid serial visual presentation (RSVP) format while EEG was recorded, with conditions including subject-verb agreement violations, semantic violations, combined violations, word-order violations, and correct control sentences. The research provides insights into the neural mechanisms underlying language comprehension in deaf populations.

Citations

1

Size

8.52 GB

Updated

Jun 26, 2026

This 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

This dataset comprises electroencephalogram (EEG) recordings from 127 young adults (18-30 years) collected during standardized cognitive tasks designed to assess perception, cognition, and action. Participants completed tasks from the Event-Related Potentials Compendium of Open Resources and Experiments (ERP CORE) or adapted variants thereof. The dataset integrates neurophysiological measures with comprehensive socioeconomic indicators spanning childhood and adulthood, including family SES, educational attainment, income, food security, and neighborhood characteristics, as well as ADHD symptom screening. This resource enables investigation of the relationship between socioeconomic context and neural electrophysiology in a diverse young adult population.

Size

10.6 GB

Updated

Jun 28, 2026

BigP3BCI Study P is a P300-based brain-computer interface dataset comprising EEG recordings from 19 healthy subjects across 2 sessions each, using a 9x8 character grid spelling paradigm. The dataset contains 32-channel EEG data sampled at 256 Hz with standardized 10-20 montage, annotated with target and non-target visual stimulus events. This derivative dataset is part of the largest public P300 BCI dataset collection, and is optimized for machine learning applications in BCI research.

Size

1.46 GB

Updated

Jun 3, 2026

This magnetoencephalography (MEG) dataset investigates the differential neural mechanisms underlying selection and maintenance of information during working memory tasks. Data from 22 participants include MEG recordings across two sessions, structural MRI, and detailed behavioral measures from a working memory task and a one-back control task involving visual Gabor stimuli. The dataset supports investigation of how the brain selectively maintains task-relevant information while filtering distractions.

Size

167 GB

Updated

Jun 21, 2026

This EEG dataset comprises recordings from 54 healthy participants performing a P300-based copy-spelling task using a 36-symbol row-column speller paradigm. The dataset includes 62 EEG channels and 4 EMG channels sampled at 1000 Hz, with offline training and online test phases designed to investigate BCI illiteracy. Data are provided in BIDS format with HED event annotations and represent a benchmark resource for evaluating brain-computer interface performance across different user populations.

Citations

100

Size

38.6 GB

Updated

Jun 3, 2026