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This dataset comprises resting-state transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) recordings from multiple subjects. The study focuses on identifying site- and stimulation-specific TMS-evoked EEG potentials using quantitative cosine similarity metrics to characterize the electrophysiological responses to TMS across different brain regions.

Participants

20

Channels

30 (10-10)

Size

44.5 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises concurrent EEG and transcranial direct current stimulation (tDCS) recordings from 40 participants performing an attentional blink task. Participants completed two laboratory sessions approximately one week apart, receiving either anodal or cathodal tDCS (1 mA, 20 minutes) while EEG was recorded before, during, and after stimulation. The dataset documents the physiological effects of tDCS on neural correlates of attention and provides a resource for investigating artifact characteristics in concurrent tDCS-EEG recordings.

Participants

47

Channels

64 (10-10)

Size

110 GB

Version

vv1.0.0

Updated

Jul 10, 2026

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

27.7 GB

Version

vv1.0.0

Updated

Jul 10, 2026

Beetl2021-B is a preprocessed 32-channel EEG dataset from the NeurIPS 2021 BEETL competition designed for benchmarking transfer learning and domain adaptation algorithms in motor imagery brain-computer interfaces. The dataset comprises EEG recordings from 2 healthy subjects performing 4-class motor imagery tasks (left hand, right hand, feet, and rest) sampled at 200 Hz, with 1590 total trials organized for cross-subject and cross-dataset generalization assessment.

Participants

2

Channels

32 (10-10)

HED

v8.4.0

Size

31.3 MB

Updated

Jul 10, 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

34.4 GB

Version

vv1.0.0

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

vv1.0.0

Updated

Jul 10, 2026

A BIDS-formatted derivative dataset of steady-state visually evoked potential (SSVEP) EEG recordings from 54 subjects across 2 sessions. This dataset comprises 21,600 trials of 4-second EEG recordings at 1000 Hz sampling frequency from 62 channels, organized for standardized brain-computer interface benchmarking. The data has been processed and converted to BIDS format using the Mother of All BCI Benchmarks (MOABB) framework, enabling reproducible analysis of SSVEP-based BCI paradigms with 4 stimulus classes.

Participants

54

Channels

62 (10-05)

Size

16.9 GB

Version

vv1.0.2

Updated

Jul 10, 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. This represents Study A of the largest public P300 BCI dataset and has been processed using the MOABB (Mother of All BCI Benchmarks) framework.

Participants

13

Channels

32 (10-10)

HED

v8.4.0

Size

868 MB

Version

vv1.0.1

Updated

Jul 10, 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

vv1.0.0

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

vv1.0.0

Updated

Jul 10, 2026