Discover datasets

This dataset contains electroencephalogram (EEG) recordings from 19 healthy participants using a brain-computer music interface designed to enable real-time control of musical tempo through motor imagery. Participants performed kinesthetic motor imagery tasks—imagining squeezing a ball to increase tempo or relaxing to decrease tempo—across nine experimental runs including a calibration phase. The data were collected at 1 kHz sampling rate with 20-second epochs synchronized to music clips, providing a resource for investigating the neural correlates of intentional tempo modulation and music-based brain-computer interface design. This dataset accompanies the publication by Daly et al. (2018). Full methodological details are available in Daly et al. (2014a, 2014b).

Participants

18

Channels

19 (10-20)

Size

2.38 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset contains neuroimaging data from an experiment investigating the neural processing of numbers, letters, and false fonts presented as single items or strings. The study employs event-related experimental design with two runs, each utilizing distinct stimulus categories marked by specific trigger codes to enable precise temporal analysis of brain responses to different visual stimuli. Data were acquired using magnetoencephalography (MEG).

Participants

25

Size

310 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 20 subjects performing a sustained attention task involving mental concentration on a target stimulus. Participants received automated verbal instructions guiding them through cycles of relaxation, preparation, and 15-second concentration periods, with the entire experiment conducted with eyes closed. The dataset includes 20 repetitions of the attention task per participant and is annotated using HED (Hierarchical Event Descriptors) version 8.0.0.

Participants

20

Channels

32 (egi-geodesic)

HED

v8.0.0

Size

776 MB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings from participants performing go-nogo categorization and detection tasks across two recording sessions. Participants viewed briefly presented images (20 ms) and responded according to task-specific rules: lifting a finger for target stimuli (go response) or maintaining button press for non-targets (nogo response). The dataset includes two complementary tasks—animal categorization and image recognition—with systematic manipulation of stimulus presentation and response requirements, providing a rich resource for investigating event-related potentials and decision-making processes.

Participants

14

Channels

31 (10-20)

Size

9.22 GB

Version

v1.0.0

Updated

Jul 10, 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.

Participants

22

Size

167 GB

Version

v1.0.0

Updated

Jul 10, 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.

Participants

17

Size

25.9 GB

Version

v1.0.0

Updated

Jul 10, 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.

Participants

10

Size

18.0 GB

Version

v1.0.0

Updated

Jul 10, 2026

OcularLDT is a neuroimaging dataset containing eye-tracking and MEG recordings acquired during a lexical decision task with ocular monitoring. The dataset provides synchronized measurements of oculomotor behavior and brain activity, enabling investigation of the relationship between eye movements and linguistic processing during word recognition tasks.

Participants

19

Size

35.0 GB

Version

v1.0.0

Updated

Jul 10, 2026

This EEG dataset comprises recordings from 18 participants engaged in auditory and visual rhythm perception tasks using an omission paradigm. The study dissociates predictive neural mechanisms from sensory-evoked activity by presenting rhythmic stimuli with occasional omissions in both auditory and visual modalities. Comprehensive preprocessing code, time-frequency analysis, frequency band extraction, and statistical procedures are provided, with cluster analysis performed using EEGLAB.

Participants

18

Channels

32 (10-10)

Size

1.94 GB

Version

v1.0.0

Updated

Jul 10, 2026

This multimodal neuroimaging dataset investigates the neural mechanisms of metacognition—the ability to assess decision confidence—by isolating postdecisional from decisional contributions. Healthy volunteers performed perceptual judgments and observed decisions while reporting confidence, with concurrent electroencephalography and functional magnetic resonance imaging recordings. The study reveals dissociable neural correlates of confidence in prefrontal regions and proposes a computational model explaining how decision commitment enhances metacognitive performance.

Participants

20

Size

423 GB

Version

v1.0.0

Updated

Jul 10, 2026