Discover datasets

This dataset comprises intraoperative intracranial EEG recordings from 10 patients undergoing brain tumor resection in the perirolandic region. Median nerve somatosensory evoked potentials (SEP) were recorded simultaneously using two 4-contact ECoG electrode strips with different impedance characteristics (low impedance: 3.4 kΩ and high impedance: 6.9 kΩ) to investigate the efficacy of low-impedance electrodes for detecting high-frequency oscillations in clinical neurophysiology.

Participants

10

Citations

9

Size

1.20 GB

Version

v1.0.0

Updated

Jul 10, 2026

A joint EEG-fMRI dataset acquired from 21 healthy adult participants during affective music listening. Participants listened to music stimuli designed to elicit different emotional responses and provided self-reported affective state ratings. This multimodal neuroimaging dataset enables investigation of the neural correlates of emotional responses to music across both electrophysiological and hemodynamic measures.

Participants

21

Channels

31 (10-10)

Size

15.3 GB

Version

v1.0.0

Updated

Jul 10, 2026

Imported from OpenNeuro ds006126

Participants

6

Channels

3 (other)

Size

1.08 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises intracranial EEG recordings from five patients undergoing single-pulse electrical stimulation of temporal and limbic circuitry at Mayo Clinic. The study investigates Basis Profile Curve identification and distinct neural responses evoked in human ventral temporal cortex through direct brain stimulation, providing insights into functional connectivity and processing speed within the human connectome.

Participants

5

Citations

15

Size

11.4 GB

Version

v1.0.0

Updated

Jul 10, 2026

Brain Treebank is a large-scale intracranial EEG dataset comprising 43 hours of iEEG recordings from 10 epilepsy patients watching naturalistic Hollywood movies, with 1,688 electrodes sampled at 2048 Hz. The dataset includes time-aligned linguistic annotations with word-level transcripts and Universal Dependencies syntax trees, providing a unique resource for studying neural language processing during naturalistic stimulation.

Participants

10

Size

508 GB

Version

v1.0.0

Updated

Jul 10, 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

This dataset comprises high-density EEG recordings from 30 healthy, right-handed, native Chinese-speaking adults performing imagined and executed vocalization and writing tasks involving four Chinese strokes and four English letters. The factorial design crossed task mode, action, script and token identity, yielding 32 event-coded conditions across 15,360 task events. The dataset supports research into motor imagery, speech and handwriting-related neural communication signals.

Participants

30

Channels

124 (10-05)

Size

181 GB

Version

v1.0.0

Updated

Sep 8, 2026

Imported from OpenNeuro ds004745

Participants

6

Channels

8 (other)

Citations

27

Size

242 MB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 15 healthy participants performing reach-and-grasp motor imagery tasks using three different electrode systems: gel-based laboratory equipment, water-based mobile EEG, and dry-electrode mobile EEG. Data were acquired at 256 Hz from 58 EEG channels plus 6 EOG channels across three sessions with 7200 total trials. Participants executed palmar grasp (toward glass) and lateral grasp (toward spoon) actions. The study investigates the feasibility of decoding natural reach-and-grasp neural correlates across different EEG acquisition modalities for brain-computer interface applications.

Participants

45

Channels

58 (10-05)

Citations

51

HED

v8.4.0

Size

8.67 GB

Version

v1.0.3

Updated

Aug 18, 2026

Imported from OpenNeuro ds004022

Participants

7

Channels

18

Citations

2

Size

635 MB

Version

v1.0.0

Updated

Jul 10, 2026