Discover datasets

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

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)

Citations

82

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

46

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

98

Size

102 GB

Version

v1.1.0

Updated

Jul 10, 2026

A polysomnographic dataset comprising 1,983 overnight sleep recordings from Massachusetts General Hospital, including 994 training subjects with expert annotations and 989 test subjects. The dataset was created for the PhysioNet/Computing in Cardiology Challenge 2018 and contains 13 channels of physiological signals (EEG, EOG, EMG, respiratory, SpO2, ECG) sampled at 200 Hz, with annotations for sleep stages, respiratory events, and arousal types in the training set.

Participants

1983

Channels

6 (other)

Citations

91

Size

401 GB

Version

v1.1.0

Updated

Jul 10, 2026

197 whole-night PSG, 100 subjects, expert 30s sleep staging (W/N1/N2/N3/REM) in events.tsv. EEG Fpz-Cz/Pz-Oz + EOG + EMG at 100Hz. PhysioNet DOI:10.13026/C2X676

Participants

100

Channels

7 (other)

Citations

89

Size

8.12 GB

Version

v1.0.0

Updated

Jul 10, 2026

2417 clinical EEG recordings, 19-ch 10-20 montage, normal/abnormal labels. EDF format. Zenodo DOI:10.5281/zenodo.10909103

Participants

2417

Channels

21 (10-20)

Size

13.8 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises scalp EEG recordings from 45 participants during passive listening to a naturalistic 12.4-minute audiobook excerpt (first chapter of Alice's Adventures in Wonderland). The study investigates how the brain constructs hierarchical syntactic structure and generates rapid linguistic predictions during continuous speech comprehension. Each word event is annotated with linguistic predictors including lexical frequency, acoustic properties, and surprisal estimates from n-gram, recurrent neural network, and context-free grammar models.

Participants

45

Channels

60 (other)

Citations

127

Size

3.81 GB

Version

v1.0.0

Updated

Jul 10, 2026