Discover datasets

NeuroMorph is a high-temporal resolution MEG dataset designed for morpheme-based linguistic analysis, comprising recordings from 24 subjects totaling over 17 hours of data. Data were collected using a KIT/Yokogawa MEG system to investigate visual language and cognitive processing, with a focus on morphological structure in written language comprehension.

Participants

24

Size

140 GB

Version

v1.0.0

Updated

Aug 19, 2026

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

2.39 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises longitudinal motor imagery EEG recordings from 18 BCI-naive subjects across six sessions (one offline, five online), designed to study transfer learning and skill acquisition in brain-computer interfaces (BCIs). It compares two domain adaptation frameworks—Generic Recentering and Personally Assisted Recentering—for calibration-free BCI training using left/right hand motor imagery with visual feedback. Data were collected at 512 Hz with 22 EEG channels and analyzed using Riemannian geometry-based classifiers.

Participants

18

Channels

22 (10-10)

Citations

49

HED

v8.4.0

Size

14.4 GB

Version

v1.0.4

Updated

Aug 20, 2026

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

This dataset comprises event-related potential (ERP) recordings from 13 healthy subjects performing a visual matrix speller task using a calibrationless brain-computer interface approach. The study introduces learning from label proportions (LLP), an unsupervised classification method that exploits known target/non-target stimulus ratios to enable online BCI operation without prior calibration. Subjects performed copy-spelling tasks using a 6×7 character grid across three sessions, achieving 84.5% character accuracy without labeled training data.

Participants

13

Channels

31 (10-10)

Citations

46

HED

v8.4.0

Size

5.15 GB

Version

v1.0.2

Updated

Aug 18, 2026

Beetl2021-B is a preprocessed EEG dataset from the NeurIPS 2021 BEETL competition, focused on transfer learning for motor imagery decoding. It contains 32-channel EEG recordings from 2 healthy subjects performing a 4-class motor imagery task (left hand, right hand, feet, rest) sampled at 200 Hz. The dataset is designed to benchmark transfer learning and domain adaptation algorithms addressing cross-subject and cross-dataset generalization challenges in brain-computer interfaces.

Participants

2

Channels

32 (10-10)

Citations

2

HED

v8.4.0

Size

1.99 GB

Version

v1.0.5

Updated

Aug 20, 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 contains EEG recordings from two experiments investigating the cortical encoding of sound source elevation in humans. Subjects were presented with noise bursts from loudspeakers positioned at various elevations in a hemi-anechoic chamber, and were also tested on their ability to localize elevated sound sources. Experiment I used a deviant detection paradigm, while Experiment II used a one-back task involving sound elevation discrimination.

Participants

53

Channels

64 (other)

Size

18.2 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises functional near-infrared spectroscopy (fNIRS) measurements of hemodynamic brain responses to painful thermal and electrical stimuli applied to the fingertip during a finger-tapping motor task. The study investigates the specificity of cerebral hemodynamic responses to noxious stimulation, combining neuroimaging with sensory stimulation protocols to characterize pain-related brain activation patterns.

Participants

11

Size

1.20 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset contains intracranial EEG (iEEG) recordings from human patients, rodents, pigs, and non-human primates acquired using novel flexible, scalable, high-resolution micro-stereo-electrodes. The study evaluates the performance of these custom-fabricated depth electrodes for neural recording and includes direct electrical stimulation experiments to assess stimulation effects on brain activity. Raw and preprocessed epoched data are provided in iEEG-BIDS format with accompanying channel maps and derivatives.

Participants

1

Citations

4

Size

1.36 GB

Version

v1.0.0

Updated

Jul 10, 2026