Discover datasets

This dataset comprises 32-channel EEG recordings collected during a sham neurofeedback experiment conducted in a virtual reality environment. Participants underwent four conditions—positive feedback, negative feedback, control, and resting-state (eyes open/closed)—designed to examine how feedback valence influences alpha-band activity during an attentional task. The data were originally recorded in NeuroScan .cnt format and converted to BIDS using MNE-BIDS.

Participants

36

Channels

29 (10-10)

Size

10.4 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises EEG recordings from two individuals with chronic post-stroke anomia who underwent an 8-week individualized transcranial alternating current stimulation (tACS) intervention. Using a single-case ABAB experimental design, EEG was recorded during picture naming tasks across alternating stimulation and non-stimulation phases and at follow-up sessions. The study aims to determine whether tACS, guided by individual EEG profiles, improves naming accuracy and latency and whether these effects persist after intervention.

Participants

2

Channels

32 (10-10)

Size

1.09 GB

Version

v1.0.0

Updated

Aug 18, 2026

This dataset contains electroencephalography (EEG) recordings from 21 participants performing a continuous feedback prediction task. Participants learned to predict the final level of an animated rising bar under varying conditions of outcome predictability (highly predictable, somewhat predictable, and unpredictable), with performance feedback provided through point rewards. The dataset includes behavioral responses, stimulus timing information, and trigger codes for experimental events, enabling investigation of neural correlates of prediction, feedback processing, and learning under uncertainty.

Participants

21

Channels

31 (10-10)

Size

3.48 GB

Version

v1.0.0

Updated

Jul 22, 2026

This dataset contains laser-evoked potential recordings capturing neural responses in the human spinal cord and cortex following nociceptive laser stimulation. It was collected to investigate the neurophysiological processing of pain-related signals across spinal and cortical levels. The dataset is organized according to the BIDS standard for electrophysiological data.

Participants

7

Channels

72 (other)

Size

18.6 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset contains 64-channel EEG recordings from 62 participants (31 pairs) who played a competitive, computerised version of the Rock-Paper-Scissors game across 480 games. The study investigates the neural correlates of competitive decision-making during real-time social interaction. This dataset supports research on neural decoding of strategic choices in a dyadic competitive context.

Participants

31

Channels

64

Citations

6

Size

78.0 GB

Version

v1.0.0

Updated

Aug 19, 2026

The RECAP-EEG dataset provides resting-state electroencephalography recordings from 21 neurotypical undergraduate students collected before and after different study interventions (active retrieval practice, passive review, and control conditions) at the Federal University of Paraíba, Brazil. The dataset aims to support research into the neural correlates of learning, retrieval practice, and educational neuroscience. Recordings include pre- and post-intervention resting-state EEG with eyes-open and eyes-closed blocks.

Participants

21

Channels

31

Citations

1

Size

1.32 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset contains electroencephalogram (EEG), galvanic skin response (GSR), and electrocardiogram (ECG) recordings from 19 healthy adult participants during a calibration session of an affective brain-computer music interface system. Participants listened to 40-second synthetic music clips designed to induce specific affective states defined by valence and arousal dimensions across 5 runs of 18 trials each. The synthetic music was generated in real-time based on target emotional states and could be modified online to induce target emotional states. The dataset includes self-reported affective state ratings and auxiliary variables, serving as calibration data for the brain-computer music interface system. This dataset is part of a three-dataset collection that includes offline training and online real-time control sessions.

Participants

19

Channels

32 (10-10)

Size

6.10 GB

Version

v1.0.0

Updated

Jul 10, 2026

Beetl2021-B is a preprocessed EEG dataset from the NeurIPS 2021 BEETL competition designed to benchmark transfer learning and domain adaptation algorithms for motor imagery decoding. The dataset comprises 32-channel 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 into 4-second epochs. This resource addresses the critical challenge of cross-subject and cross-dataset generalization in brain-computer interface applications.

Participants

2

Channels

32 (10-10)

Citations

2

HED

v8.4.0

Size

1.99 GB

Version

v1.0.4

Updated

Aug 18, 2026

An EEG dataset of imagined speech from 15 healthy participants comparing traditional cue-based and gamified paradigm designs for brain-computer interface applications. The dataset comprises 1,800 trials of four Spanish directional commands recorded at 500 Hz from 24 electrodes using the mBrainTrain Smarting system. This derivative dataset enables systematic evaluation of paradigm design effects on imagined speech decoding performance.

Participants

15

Channels

24 (10-10)

Citations

1

HED

v8.4.0

Size

1.52 GB

Version

v1.0.3

Updated

Aug 18, 2026

Imported from OpenNeuro ds005028

Participants

11

Citations

2

Size

1.46 GB

Version

v1.0.0

Updated

Jul 10, 2026