Discover datasets

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

This dataset comprises preprocessed EEG recordings from 12 healthy participants performing a brain-computer interface task based on broad-band visually evoked potentials (BBVEPs). Participants controlled a 6×6 matrix speller by attending to target symbols while cells were stimulated with pseudo-random Gold code sequences. The study demonstrates a novel reconvolution approach for template generation that achieves 86% online accuracy and an information transfer rate of 48 bits per minute.

Participants

12

Channels

64 (10-10)

Citations

72

HED

v8.4.0

Size

11.0 GB

Version

v1.0.3

Updated

Aug 18, 2026

This dataset comprises EEG recordings from 33 participants who completed counterbalanced visual and auditory surprise tasks designed to probe electrophysiological markers of surprise-induced failures of perceptual awareness. The data support an ongoing study by Leong et al. examining how unexpected stimuli disrupt conscious awareness across sensory modalities. Some participants' data are incomplete due to exclusion at various analysis stages.

Participants

33

Channels

16 (10-20)

Size

535 MB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises electrophysiological recordings (EEG, ECG, EMG) collected from 9 adult burn patients in an intensive care unit during Music-Assisted Relaxation therapy sessions. As part of a randomized clinical trial (NCT04571255), participants underwent two recording sessions with pre-intervention baseline, intervention, and post-intervention phases. The study investigates the physiological effects of music therapy on pain perception and anxiety-depression levels in critically ill burn patients using clinical-grade equipment with standardized electrode montages.

Participants

9

Channels

8

Citations

1

Size

1.75 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset (eldBETA) provides 64-channel SSVEP-BCI EEG recordings from 100 elderly participants (aged 51-81, mean 63.17 years) performing a 9-target speller task using joint frequency and phase modulation (JFPM) stimuli. Each subject completed 7 sessions of 7 blocks with 9 trials each, recorded at 1000 Hz using a Synamps2 (Neuroscan) amplifier with a Cz reference. The dataset is intended as a benchmark for SSVEP-BCI algorithms in aging populations and has been converted to BIDS format with HED event annotations for use in the MOABB benchmarking framework.

Participants

100

Channels

64 (10-10)

Citations

29

HED

v8.4.0

Size

34.8 GB

Version

v1.0.3

Updated

Aug 20, 2026

Imported from OpenNeuro ds004018

Participants

16

Citations

98

Size

10.5 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises multimodal physiological recordings from 86 participants during resting state and a digit span working memory task. It includes 64-channel EEG, electrocardiography, photoplethysmography, pupillometry, and behavioral performance data. The dataset enables investigation of neural and peripheral physiological correlates of cognitive load, working memory capacity, and cognitive overload detection across fine temporal scales.

Participants

86

Channels

63 (10-10)

Citations

61

Size

253 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 16 healthy participants performing a code-modulated visual evoked potential (c-VEP) brain-computer interface task using p-ary m-sequences. The study investigates non-binary m-sequence stimulation patterns to enhance user comfort in c-VEP-based BCIs. Data were collected across 5 sessions per subject with 8 runs per session at 256 Hz sampling rate using 16 EEG channels, providing a comprehensive resource for BCI paradigm development and evaluation.

Participants

16

Channels

16 (10-05)

Citations

28

HED

v8.4.0

Size

1.71 GB

Version

v1.0.4

Updated

Aug 18, 2026

A derivative EEG dataset containing checkerboard m-sequence-based code-modulated visual evoked potential (c-VEP) recordings from 16 healthy participants across 8 sessions. This dataset is derived from the source dataset by Martínez-Cagigal et al. (2025) (DOI: 10.71569/7c67-v596) and is described in the publication by Fernández-Rodríguez et al. (2023) in Frontiers in Human Neuroscience. The dataset evaluates the influence of spatial frequency in visual stimuli for brain-computer interface applications, with 16-channel EEG data sampled at 256 Hz during a two-class visual stimulation paradigm.

Participants

16

Channels

16 (10-05)

Citations

6

HED

v8.4.0

Size

1.38 GB

Version

v1.0.4

Updated

Aug 18, 2026

Imported from OpenNeuro ds004368

Participants

39

Channels

63 (10-10)

Size

969 MB

Version

v1.0.0

Updated

Jul 10, 2026