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The Brain, Body, and Behaviour Dataset - Experiment 3 is a multimodal neurophysiological dataset comprising 29 subjects across 2 sessions designed to investigate the effects of attentional state on learning from educational videos. Participants watched six educational videos under two conditions: attentive (with post-video testing) and distracted (with concurrent cognitive load), while simultaneous recordings of brain activity, cardiovascular function, eye movements, and head motion were collected. The dataset includes concurrent EEG, ECG, EOG, gaze tracking, pupil size, and head position data, along with behavioral measures including memory questionnaires and ADHD symptom assessments.

Participants

29

Channels

64 (10-10)

Size

7.44 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises simultaneous EEG and fNIRS recordings from 12 participants performing semantic imagery tasks involving silent naming and sensory-based imagination of animals and tools. Participants engaged in visual, auditory, and tactile perception tasks while neural activity was captured using a 64-channel BioSemi EEG system and a NIRx fNIRS imaging system with integrated optodes. The multimodal neuroimaging data supports research in semantic decoding and brain-computer interface applications.

Participants

12

Channels

64 (biosemi)

Size

24.2 GB

Version

v1.0.0

Updated

Jul 10, 2026

Participants

37

Channels

32 (10-10)

HED

v8.4.0

Size

2.59 GB

Version

v1.0.0

Updated

Jul 10, 2026

This magnetoencephalography (MEG) dataset investigates neural object representations during dynamic visual occlusion. Participants viewed objects that were either occluded or disappeared while their neural activity and eye movements were recorded. The dataset includes raw MEG data, behavioral responses, eye-tracking recordings, and preprocessed neural signals epoched relative to stimulus onset and position changes, enabling investigation of how the brain maintains object representations under conditions of visual disruption.

Participants

27

Size

258 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset (WBCIC-SHU) contains multi-day EEG recordings from 51 healthy, right-handed, BCI-naive subjects performing a motor imagery brain-computer interface paradigm across three sessions per subject. Participants imagined left-hand, right-hand, or (for a subset of 11 subjects) foot movements in response to visual and auditory cues, yielding 39,600 trials in total. The dataset is intended for benchmarking motor imagery classification algorithms such as CSP, FBCSP, EEGNet, deepConvNet, and FBCNet, and was converted to BIDS format using MOABB.

Participants

51

Channels

59 (10-10)

Citations

34

HED

v8.4.0

Size

154 GB

Version

v1.0.5

Updated

Aug 18, 2026

This EEG dataset investigates the neural dynamics of visual object coding across brain hemispheres. Object and word stimuli were rapidly presented (5Hz) in the left visual field, right visual field, or centrally, while participants engaged in an orthogonal red-target detection task. The dataset supports investigation of within- and cross-hemisphere object representation dynamics using frequency-tagging EEG paradigms.

Participants

20

Citations

11

Size

58.8 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises 382 electroencephalographic recordings from 19 participants performing a P300 visual oddball paradigm with systematically varied stimulus properties. Participants completed three experimental sessions, each containing seven recordings with different numbers of visual stimuli (4-9) and flash types (standard flash or cartoon face overlay). EEG data were acquired from eight scalp electrodes at 256 Hz sampling rate, with detailed event markers encoding experimental phases and stimulus presentations.

Participants

19

Channels

9 (10-10)

Citations

3

Size

1.27 GB

Version

v1.0.0

Updated

Jul 22, 2026

This dataset contains EEG recordings from 12 healthy human subjects performing three-class motor imagery tasks involving the same upper extremity: rest, imagined grasping, and imagined elbow flexion. Data were collected across four sessions per subject using a 32-channel EEG system, and the dataset is a BIDS-formatted derivative of the original study on classifying imaginary motor states using time-domain features. The dataset supports research in brain-computer interfaces (BCI) for motor rehabilitation applications.

Participants

12

Channels

32 (egi-geodesic)

Citations

44

HED

v8.4.0

Size

52.0 GB

Version

v1.0.3

Updated

Aug 20, 2026

This dataset comprises EEG recordings from 39 Italian-speaking participants performing a picture-matching task under four cue conditions (Physical, Imagery, Literal, and Metaphorical), designed to investigate how mental representations generated by verbal and non-verbal cues influence the processing of subsequent target pictures. The study, part of the ERC-funded PROMENADE project, focuses on the neurocognitive processing of metaphorical versus literal language and mental imagery. EEG data were collected in long epochs around target picture onset, alongside behavioral accuracy and questionnaire-based measures of vocabulary and imagery ability.

Participants

39

Channels

58 (10-10)

Size

14.8 GB

Version

v1.0.0

Updated

Aug 19, 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