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BigP3BCI Study B is a derivative EEG dataset containing P300 brain-computer interface recordings from 19 ALS patients across 8 sessions each, using a 6x6 checkerboard visual speller paradigm. The dataset comprises 16-channel EEG data sampled at 256 Hz, annotated with target and non-target event classifications using HED 8.4.0 schema. This dataset is a subset of the larger BigP3BCI collection, the largest public P300 BCI dataset with recordings from approximately 267 subjects across multiple studies.

Participants

19

Channels

16 (10-10)

HED

v8.4.0

Size

4.28 GB

Version

v1.0.2

Updated

Aug 18, 2026

The Brain, Body, and Behaviour Dataset (Experiment 1) is a multimodal neuroimaging dataset comprising eye-tracking recordings from 27 subjects across two sessions during educational video viewing. Subjects watched five informative videos under two conditions: an attentive condition with post-video comprehension testing, and a distracted condition with concurrent cognitive load (backward counting). The dataset includes gaze coordinates, pupil size, blinks, saccades, and fixations, along with behavioral questionnaires assessing domain knowledge and memory retention, providing a resource for studying attention, learning, and cognitive engagement during multimedia presentation.

Size

880 MB

Version

v1.0.0

Updated

Jul 10, 2026

The Brain, Body, and Behaviour Dataset (Experiment 4) is a multimodal neurophysiological dataset comprising simultaneous recordings of EEG, eye-tracking, cardiac, respiratory, and electrooculographic signals from 43 subjects across two sessions. Participants watched three educational videos (Stim-04, Stim-05, Stim-06) under two attention conditions. In Session 1 (attentive condition), participants viewed the videos and answered comprehension questions afterward. In Session 2 (distracted condition), participants viewed the same three videos in the same order while performing a concurrent backward counting task, with no comprehension testing. This derivative dataset supports investigation of neural and behavioral correlates of attention, learning, and cognitive load during naturalistic video viewing.

Citations

4

Size

5.12 GB

Version

v1.0.0

Updated

Jul 10, 2026

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

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

15

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

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)

Citations

82

Size

198 GB

Version

v1.1.0

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

98

Size

102 GB

Version

v1.1.0

Updated

Jul 10, 2026