Discover datasets

This dataset comprises EEG recordings from 25 college-age participants performing a reinforcement learning task with affective feedback. Collected in 2019 at the University of New Mexico's Clinical Research Center Lab, the data were acquired to investigate the sensitivity of the reward positivity (RewP) component to affective liking during probabilistic learning. This represents Experiment #2 of a study examining neural correlates of reward processing and emotional valuation in decision-making. This is a re-hosted version of OpenNeuro dataset ds003753.

Participants

25

Channels

66 (10-10)

Citations

1

Size

4.62 GB

Version

v1.0.0

Updated

Jul 22, 2026

The Dataset on Emotion with Naturalistic Stimuli (DENS) comprises multimodal physiological recordings from 40 participants exposed to emotionally evocative video stimuli. The dataset includes concurrent EEG, ECG, and EMG measurements alongside behavioral self-assessment ratings across multiple emotion dimensions (valence, arousal, dominance, liking, familiarity, relevance, and emotion category). This resource supports research on emotion recognition, affective neuroscience, and the neural correlates of emotional responses to naturalistic, contextually-rich stimuli.

Participants

40

Channels

128

Citations

4

Size

4.46 GB

Version

v1.0.0

Updated

Jul 10, 2026

A longitudinal EEG dataset from 13 infants recorded at 4, 8, and 12 months of age, comprising 48 recording sessions. The dataset includes test-retest reliability assessments at 4 months (11 infants with complete longitudinal data, 2 infants with reliability data only) and was used to develop and validate APPLESEED, an automated preprocessing pipeline for estimating scale-wise entropy from pediatric EEG data. This example dataset accompanies the 2022 APPLESEED pipeline publication in Developmental Cognitive Neuroscience and demonstrates the application of entropy-based EEG analysis methods in early human development. The dataset is part of a larger longitudinal study initially described in Puglia et al. (2020).

Participants

13

Channels

32 (10-10)

Citations

14

Size

10.2 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises intracranial EEG recordings from a single patient undergoing single-pulse electrical stimulation, collected at Mayo Clinic as part of an NIH-funded initiative to develop a comprehensive database of stimulation responses. The study aims to advance understanding of human brain connectivity and network responses to electrical stimulation across the lifespan through systematic analysis of evoked potentials and network effects.

Participants

1

Citations

3

Size

620 MB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises electroencephalography (EEG) recordings investigating the neural correlates of syntactic structure and lexical properties using frequency tagging methodology. Participants were presented with linguistic stimuli while EEG activity was recorded to identify frequency-specific neural responses associated with different linguistic features. The dataset provides raw neurophysiological data suitable for studying the temporal dynamics of language processing at the neural level.

Participants

34

Citations

1

Size

195 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings from a visual working memory task presented in virtual reality, where participants remembered object details cued by either a social (avatar) or non-social (stick) agent. The study investigates how social versus non-social cues modulate neural signatures of working memory, with cue validity (congruent vs. incongruent) manipulated across trials. Raw and preprocessed EEG data are provided alongside behavioral performance metrics and detailed event codes for memory encoding, maintenance, and retrieval phases.

Participants

47

Channels

59 (10-10)

Citations

5

Size

60.9 GB

Version

v1.0.0

Updated

Jul 10, 2026

MEGMEM is a magnetoencephalography (MEG) dataset acquired during a memory task designed to investigate neural mechanisms of episodic memory encoding and retrieval. The dataset contains raw MEG recordings from multiple subjects performing structured memory experiments, providing a resource for studying oscillatory dynamics and neural correlates of memory processes.

Participants

28

Citations

3

Size

218 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises synchronized electroencephalogram (EEG), electrocardiogram (ECG), and pupillography recordings from 75 participants (36 young and 39 older adults) during auditory cued reaction time tasks and passive listening conditions. Participants performed a simple reaction time task and a go/no-go task, with EEG acquired at 500 Hz using 64 scalp electrodes, ECG recorded bipolarly from chest electrodes, and pupil data sampled at 240 Hz. The dataset is designed to investigate age-related differences in neural, cardiac, and pupillary responses during cognitive and perceptual decision-making tasks.

Participants

75

Channels

61 (10-10)

Citations

30

Size

21.5 GB

Version

v1.0.0

Updated

Jul 10, 2026

This open multimodal dataset combines intracranial electroencephalography (iEEG) and functional magnetic resonance imaging (fMRI) recordings from patients during naturalistic audiovisual film stimulation. The dataset includes simultaneous electrocorticography and fMRI data collected during a short film presentation, providing a unique resource for investigating neural responses to complex naturalistic stimuli across multiple recording modalities and spatial scales.

Participants

63

Citations

39

Size

15.3 GB

Version

v1.0.0

Updated

Jul 10, 2026

This derivative dataset contains processed MEG data from a study investigating the neural mechanisms of model-based aversive learning in humans. The research examines how task state representations are preferentially reactivated during aversive learning, providing insights into the computational and neural basis of threat-related decision-making and learning processes.

Participants

28

Size

302 GB

Version

v1.0.0

Updated

Jul 22, 2026