Discover datasets

EEG dataset accompanying the PLOS ONE article 'Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer's risk classification' (Henao Isaza, Aguillon, Tobón Quintero, Lopera, & Ochoa Gómez, 2026). The recordings support a methodology for enriching samples in biomarker studies that classify Alzheimer's disease risk from EEG features. Data are stored in BIDS format (BrainVision .vhdr/.eeg/.vmrk files with accompanying metadata) and released under a CC0 license.

Participants

44

Channels

58 (10-10)

Citations

1

Size

13.6 GB

Version

v1.0.0

Updated

Jun 30, 2026

MorseEEG-ATP is a multitask EEG dataset for studying auditory temporal processing and temporal pattern discrimination using Morse code stimuli. It contains synchronized 62-channel EEG and behavioral data from 32 healthy adults performing dot-count and first-last match discrimination tasks at three presentation rates (15, 30, and 40 characters per minute). The release includes raw and minimally preprocessed EEG, event annotations, behavioral measures, and analysis scripts in an EEG-BIDS structure.

Participants

32

Channels

62 (10-10)

Size

14.6 GB

Version

v1.0.0

Updated

Jun 30, 2026

MULTI-CLARID is a combined fMRI/EEG dataset acquired during an auditory category learning task and resting state. It includes 63-channel EEG with ECG, EOG, facial EMG and skin conductance, plus respiration and finger-pulse recordings, and T1-weighted and UTE anatomical MR scans. Five participants' datasets containing the multi-dimensional category learning experiment are currently shared.

Participants

34

Channels

72 (10-10)

Size

6.43 GB

Version

v1.0.0

Updated

Jun 27, 2026

This dataset contains EEG recordings from 7 participants performing an auditory imagery task, in which they imagined the sounds made by objects from the semantic categories of animals and tools for 5 seconds. It supports research on semantic decoding and brain-computer interfacing. Recordings were made with a 64-channel BioSemi system, with additional EOG, physiological, and respiration channels.

Participants

7

Channels

64 (10-10)

Size

12.7 GB

Version

v1.0.0

Updated

Jun 24, 2026

EEG dataset of a visual oddball (P300) task from the PURSUE project, collected in 2017 and 2018 from participants at three primarily undergraduate institutions in Southern California, Massachusetts, and Virginia. The task design follows the ERP CORE paradigm, and the dataset includes participant demographic information such as race.

Participants

286

Channels

32 (10-10)

Size

7.84 GB

Version

v1.0.0

Updated

Jun 3, 2026

This dataset contains raw and processed magnetoencephalography (MEG) recordings from a study of model-based aversive learning in humans. The study examines how learning about aversive outcomes is supported by preferential reactivation of task states. Raw data are provided as .fif files, alongside derived processed data.

Participants

28

Citations

3

Size

302 GB

Version

v1.0.0

Updated

Jun 22, 2026

MEG dataset from 22 participants performing a visual working memory task in which two Gabor stimuli were presented and a retro-cue indicated which stimulus and attribute to maintain for comparison with a probe. Each participant was recorded in two MEG sessions, with a one-back control task and anatomical T1 MRI. The data support investigation of the distinct brain mechanisms of selecting and maintaining information in working memory.

Participants

22

Size

168 GB

Version

v1.0.0

Updated

Jun 21, 2026

EEG dataset from a masked-priming-style ERP study examining neural associations between fingerspelling, printed English words, and American Sign Language signs in deaf readers. Participants viewed 300 prime-target pairs in which primes were English words, ASL signs, or fingerspelled words, followed by four-letter English word targets, while performing an animal-detection task. The data support investigation of cross-modal and cross-script priming effects in deaf readers.

Participants

24

Channels

32 (10-10)

Size

2.62 GB

Version

v1.0.0

Updated

Jun 27, 2026

EEG recordings from 13 healthy participants performing a copy-spelling task with a visual 6x7 matrix ERP speller. The speller was designed for learning from label proportions (LLP), an unsupervised, calibrationless classification approach for ERP-based brain-computer interfaces. Two stimulus sequences with different target/non-target ratios allowed LLP to reconstruct mean target and non-target responses without labeled data. In the original online study, 84.5% of characters were spelled correctly on average without prior calibration.

Participants

13

Channels

31 (10-10)

Citations

46

HED

v8.4.0

Size

5.15 GB

Version

v1.0.3

Updated

Oct 8, 2026

This EEG dataset investigates how personalized smartphone notification sounds bias auditory salience across successive stages of sensory and cognitive processing. Participants completed an auditory oddball experiment, allowing comparison of neural responses to personally relevant notification sounds against other auditory stimuli.

Participants

57

Channels

64 (10-10)

Size

42.5 GB

Version

v1.0.0

Updated

Jun 30, 2026