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This dataset contains electroencephalographic (EEG) and electrocorticographic (ECoG) recordings from twenty healthy participants and one patient with intractable epilepsy during a tactile detection task with confidence reporting. The study investigates the relationship between evidence accumulation, perceptual consciousness, and metacognitive monitoring using vibrotactile stimuli applied to the wrist with adaptive stimulus intensity. Data were acquired using 62-channel EEG (10-20 montage) at 1024 Hz for healthy subjects and 24-channel ECoG at 2048 Hz for the patient.

Participants

19

Channels

69 (10-10)

Size

27.9 GB

Version

v1.0.0

Updated

Jul 10, 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

An electroencephalography (EEG) dataset acquired using BioSemi equipment to investigate implicit learning processes. This raw neuroimaging dataset contains EEG recordings organized according to the Brain Imaging Data Structure (BIDS) standard, facilitating standardized analysis and sharing of electrophysiological data.

Participants

61

Size

114 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises electroencephalographic recordings from 32 participants performing motor imagery tasks during sit-to-stand and stand-to-sit transitions in both offline and online brain-computer interface (BCI) paradigms. Participants completed guided motor imagery trials while seated or standing, with EEG signals recorded from 17 channels at 250 Hz. The dataset includes offline calibration phases used to train machine learning classifiers and corresponding online validation phases where real-time BCI decoding was performed, providing a comprehensive resource for investigating neural correlates of postural transitions and BCI performance.

Participants

32

Channels

17 (10-10)

Size

2.03 GB

Version

v1.0.0

Updated

Jul 10, 2026

A BIDS-formatted derivative dataset of steady-state visually evoked potential (SSVEP) EEG recordings from 54 subjects across 2 sessions, containing 21,600 trials recorded at 1000 Hz with 62 channels. This dataset, derived from the Lee et al. 2019 source dataset, has been standardized using the Mother of All BCI Benchmarks (MOABB) framework to enable reproducible benchmarking and analysis of brain-computer interface paradigms based on SSVEP stimulation.

Participants

54

Channels

62 (10-05)

Citations

100

Size

72.4 GB

Version

v1.0.7

Updated

Aug 18, 2026

BigP3BCI Study A is a derivative P300 brain-computer interface dataset containing 32-channel EEG recordings from 13 healthy subjects performing a visual speller task using a 6×6 checkerboard stimulus paradigm. The dataset comprises EEG data sampled at 256 Hz with binary classification of target and non-target events, annotated using HED 8.4.0 schema and processed through the MOABB framework.

Participants

13

Channels

32 (10-10)

HED

v8.4.0

Size

3.86 GB

Version

v1.0.3

Updated

Aug 18, 2026

The YOTO (You Only Think Once) dataset is a human electroencephalography resource comprising high-resolution EEG recordings from 20 participants performing multisensory perception and mental imagery tasks. Signals were acquired at 1000 Hz sampling rate during exposure to unimodal (visual and auditory) and multimodal stimuli, with participants providing subjective vividness ratings. Technical validation through event-related potentials and power spectral density analyses confirmed distinct neural responses across stimulus conditions, supporting applications in neural decoding, perception, and cognitive modeling.

Participants

20

Channels

30

Size

9.91 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises neurophysiological recordings from 39 participants who received calibrated laser stimuli while rating pain intensity and unpleasantness. Participants were presented with laser stimulations at individually-determined energy levels and provided subjective pain ratings on a 0-10 numeric rating scale. The study investigates the neural mechanisms underlying pain perception and assessment, with potential applications to understanding movement-induced hypoalgesia and related pain modulation phenomena. This is a derivative dataset based on OpenNeuro ds005289.

Participants

39

Channels

64 (10-10)

Size

22.1 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises resting-state, eyes-closed scalp EEG recordings from 88 subjects: 36 with Alzheimer's disease, 23 with Frontotemporal Dementia, and 29 healthy controls. Recordings were acquired using a Nihon Kohden EEG 2100 device with 19 electrodes at 500 Hz sampling rate, totaling approximately 1164 minutes of data. The dataset includes both raw and preprocessed versions with artifact correction, supporting machine learning and clinical research in dementia diagnosis and neurophysiological characterization.

Participants

88

Channels

19 (10-20)

Citations

232

Size

5.38 GB

Version

v1.0.0

Updated

Jul 10, 2026

A high-density 124-channel EEG dataset comprising event-related potentials (ERPs) from 10 healthy participants during a visual object recognition task. Participants viewed 5,184 photographs from six object categories (human body, human face, animal body, animal face, fruit/vegetable, and inanimate objects), with 72 photographs per category, presented for 500 ms each. The dataset is suitable for investigating neural representations of object categories through single-trial EEG classification and representational similarity analysis.

Participants

10

Channels

124 (egi-geodesic)

Citations

93

HED

v8.4.0

Size

3.40 GB

Version

v1.0.3

Updated

Aug 18, 2026