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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

The Brain, Body, and Behaviour Dataset (Experiment 2) is a multimodal neurophysiological dataset comprising 31 subjects across 2 sessions designed to investigate incidental learning and attentional modulation. Participants watched five educational videos under attentive and distracted conditions while simultaneous recordings of EEG, ECG, EOG, eye-tracking, and pupil dynamics were acquired. This derivative dataset includes preprocessed physiological signals and behavioral responses to memory questionnaires, providing a comprehensive resource for studying the neural and physiological correlates of attention, learning, and cognitive load.

Participants

31

Channels

64 (10-10)

Size

5.23 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises simultaneous EEG-fMRI recordings from 22 participants (ages 23-51) during naturalistic viewing, collected at the Nathan Kline Institute. The 64-channel EEG data (61 cortical channels, 2 EOG, 1 ECG) was acquired concurrently with fMRI, supplemented by eye-tracking and respiratory recordings. The dataset includes demographic information and behavioral assessments (sleep quality, caffeine intake) to investigate correlations between electrical brain activity and hemodynamic fluctuations during naturalistic stimulation.

Participants

22

Channels

61 (10-10)

Citations

40

Size

256 GB

Version

v1.0.0

Updated

Jul 10, 2026

Brain Treebank is a large-scale intracranial EEG dataset comprising 43 hours of iEEG recordings from 10 epilepsy patients watching naturalistic Hollywood movies, with 1,688 electrodes sampled at 2048 Hz. The dataset includes time-aligned linguistic annotations with word-level transcripts and Universal Dependencies syntax trees, providing a unique resource for studying neural language processing during naturalistic stimulation.

Participants

10

Size

508 GB

Version

v1.0.0

Updated

Jul 10, 2026

VocalMind is a stereotactic EEG (sEEG) dataset comprising intracranial recordings from one participant performing vocalized, mimed, and imagined speech tasks in Mandarin Chinese, a tonal language. The dataset supports research on speech decoding, brain-computer interfaces, and neural mechanisms of overt and covert speech production. Raw recordings sampled at 1000 Hz were converted to BIDS iEEG format with event markers indicating stimulus onsets for each speech modality.

Participants

1

Citations

14

Size

1.89 GB

Version

v1.0.0

Updated

Jul 10, 2026

Participants

2

Size

1.87 GB

Version

v1.0.0

Updated

Jul 10, 2026

Participants

85

Size

126 GB

Version

v1.0.0

Updated

Jul 10, 2026

THINGS-EEG2 is a large-scale EEG dataset comprising recordings from 10 subjects viewing 16,540 distinct training images and 200 test images presented via rapid serial visual presentation at 5 Hz. The dataset includes 63-channel EEG data sampled at 1000 Hz across 4 sessions per subject, with approximately 32,540 training trials and 16,000 test trials, designed to support computational modeling of human visual object recognition. Stimuli are drawn from the THINGS database, and the dataset includes resting-state recordings and behavioral annotations for each trial.

Participants

10

Channels

63 (10-10)

Size

198 GB

Version

v1.1.0

Updated

Jul 10, 2026

MEG-MASC is a high-quality magnetoencephalography dataset comprising raw MEG recordings from 27 English speakers listening to approximately two hours of naturalistic stories from the Manually Annotated Sub-Corpus (MASC). The dataset includes precise temporal annotations of word and phoneme onsets/offsets, organized according to the Brain Imaging Data Structure (BIDS) standard. This benchmark dataset enables large-scale encoding and decoding analyses of neural responses to natural speech processing, with accompanying code for validation analyses including temporal decoding of phonetic features and word frequency effects.

Participants

27

Citations

42

Size

99.3 GB

Version

v1.0.1

Updated

Jul 10, 2026

A large-scale multi-laboratory replication study (N=356 participants across 9 UK institutions) investigating probabilistic prediction in language comprehension using event-related potentials. Participants read sentences word-by-word while EEG was recorded, with critical manipulations of indefinite articles (a/an) preceding expected or unexpected nouns. The study challenges strong prediction accounts by demonstrating reliable N400 effects on target nouns but not on preceding articles, contrary to the original DeLong et al. (2005) findings.

Participants

356

Channels

63 (10-10)

Citations

97

Size

102 GB

Version

v1.1.0

Updated

Jul 10, 2026