Discover datasets

This dataset presents multimodal simultaneous fMRI/EEG data collected from participants performing an auditory category learning task, alongside resting-state fMRI scans. Each dataset includes 63-channel EEG with additional ECG, EOG, facial EMG, and skin conductance recordings, a physiological file capturing respiration and finger-pulse signals, and structural MRI (T1-weighted and PD-weighted UTE) scans for anatomical reference and electrode localization. Collected at the Combinatorial NeuroImaging core facility of the Leibniz Institute for Neurobiology, the data support research into multi-dimensional auditory category learning and its neural correlates.

Participants

34

Channels

72 (10-10)

Citations

9

Size

6.43 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises high-density functional near-infrared spectroscopy (fNIRS) measurements acquired during a ball-squeeze motor task. The study focuses on optimizing surface-based image reconstruction techniques for high-density fNIRS neuroimaging, with data collected from multiple subjects performing controlled motor activities. The dataset supports methodological research in fNIRS signal processing and brain imaging reconstruction algorithms.

Participants

17

Size

20.7 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

Participants

4

Channels

14 (10-10)

Citations

82

Size

152 MB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises raw multimodal recordings from 30 healthy right-handed adults performing a hierarchy of cognitive, motor, and combined cognitive-motor tasks across three sessions. Data include EEG, fNIRS, ECG, EMG, torque/kinematic, behavioral (push-button), and subjective (sleepiness, cognitive load) measures, organized according to the BIDS standard. The dataset supports research into neurophysiological correlates of cognitive-motor interactions and dual-task performance.

Participants

30

Channels

32 (10-10)

Citations

1

Size

4.21 GB

Version

v1.0.0

Updated

Aug 18, 2026

A multicenter intracranial electroencephalography (iEEG) dataset comprising segmented 3-second single-channel clips from epilepsy patients, annotated for graphoelement classification and artifact detection. The dataset includes clinical metadata such as seizure onset zone (SOZ) flags, electrode anatomy, and reviewer annotations, making it suitable for developing and validating automated signal classification algorithms in clinical neurophysiology.

Participants

14

Size

20.8 GB

Version

v1.0.2

Updated

Jul 10, 2026

A preprocessed EEG dataset comprising motor imagery recordings from 14 healthy participants performing imagined right-hand tapping, left-hand tapping, and rest tasks. The dataset contains 1,665 trials acquired at 1024 Hz using a 19-channel montage with standard 10-20 electrode placement. Data have been preprocessed with ICA artifact removal and frequency filtering, and include annotations for motor imagery classification and brain-computer interface applications.

Participants

14

Channels

19 (10-20)

Citations

9

HED

v8.4.0

Size

1.23 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises electroencephalographic recordings from 12 healthy subjects during music listening with and without a passive head-mounted display (PHMD). The study employed a 16-channel EEG system sampling at 512 Hz. A passive head-mounted display is a head-mounted device without integrated electronics, consisting of a smartphone holder. The dataset provides raw, unfiltered EEG data in mat and csv formats, designed to support research on the effects of head-mounted displays and auditory stimuli on brain activity. This is a BIDS-converted derivative of the original Zenodo dataset (10.5281/zenodo.2617084).

Participants

12

Channels

16 (10-10)

Citations

19

HED

v8.4.0

Size

516 MB

Version

v1.0.5

Updated

Aug 18, 2026

This dataset comprises EEG recordings from a P300 visual matrix speller study comparing three unsupervised learning methods (Expectation-Maximization, Learning from Label Proportions, and their combination MIX) for brain-computer interface decoding. Twelve healthy participants performed a copy-spelling task using a modified 6×6 character grid extended with 10 # symbols as visual blanks (46 total symbols), recorded at 1000 Hz from 31 EEG channels. The study demonstrates that unsupervised learning methods can achieve performance comparable to supervised approaches without requiring calibration data.

Participants

12

Channels

31 (10-10)

Citations

26

HED

v8.4.0

Size

4.81 GB

Version

v1.0.2

Updated

Aug 18, 2026

A multimodal EEG dataset comprising motor imagery and motor execution data from 25 healthy subjects performing 11 intuitive upper-limb movement tasks (6 reaching, 3 grasping, 2 wrist twisting) across 3 sessions. The dataset includes 71-channel EEG recordings (60 EEG, 4 EOG, 7 EMG channels) sampled at 1000 Hz with synchronized behavioral annotations, designed for brain-computer interface research and motor control applications. This is a BIDS-formatted derivative dataset converted from the original Jeong et al. 2020 publication using MOABB (Mother of All BCI Benchmarks).

Participants

25

Channels

71 (10-10)

Citations

82

HED

v8.4.0

Size

326 GB

Version

v1.0.2

Updated

Aug 18, 2026