Discover datasets

This dataset comprises EEG data from a reinforcement learning probabilistic selection task (PST) administered to 50 healthy adult participants, half of whom underwent a sad mood manipulation and half a happy mood manipulation prior to task performance. The task, adapted from a published probabilistic selection paradigm, included training and testing phases with mood induction occurring before each training block. Data were collected between 2019 and 2021 at the Cognitive Rhythms and Computation Lab at the University of New Mexico to study the interaction between mood states and reinforcement learning processes.

Participants

50

Channels

66 (10-10)

Citations

1

Size

18.3 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises electroencephalography (EEG) recordings from 50 healthy control participants performing a reinforcement learning task under two mood conditions: sad mood manipulation (n=25) and neutral mood manipulation (n=25). The task included training and testing phases adapted from established reinforcement learning paradigms. Data were collected between 2019-2021 at the Cognitive Rhythms and Computation Lab at the University of New Mexico to investigate the effects of mood state on learning and decision-making processes.

Participants

50

Channels

66 (10-10)

Citations

1

Size

9.81 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises electroencephalography (EEG) recordings from 13 participants performing semantic imagination and perception tasks across visual and auditory modalities. Participants were presented with stimuli from three semantically dissimilar categories (guitar, flower, penguin) in pictorial, orthographic, and auditory formats. The dataset includes raw, preprocessed, and epoched EEG data acquired using a 124-channel ANT-Neuro system, providing a resource for investigating neural correlates of semantic processing and cross-modal perception.

Participants

12

Channels

124 (10-05)

Citations

1

Size

79.1 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises two reversal learning tasks designed to investigate differential neural processing of reward gain and punishment avoidance. Participants performed parallel tasks using distinct reinforcers: monetary reward (gain/non-gain outcomes) and primary threat avoidance (noise burst avoidance/application), with noise intensity individually titrated to match subjective value of monetary rewards. The dataset enables investigation of how the brain differentially encodes appetitive versus aversive learning contingencies.

Participants

26

Channels

66 (10-10)

Citations

20

Size

31.5 GB

Version

v1.0.0

Updated

Jul 10, 2026

A large-scale Spanish EEG dataset comprising 60 sessions from 56 healthy participants recorded using 64-channel EEG during speech perception and silent speech production tasks. Participants listened to and silently repeated 30 daily-use Spanish sentences. This dataset enables development of subject-independent deep neural network models for perceived sentence classification, with applications to speech decoding and neuroprosthetics for individuals with speech disorders.

Participants

56

Channels

64 (10-10)

Size

25.2 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises MEG recordings collected to investigate the sustained neural representations of personally familiar people and places during a cued recall task. Participants were cued to recall familiar individuals and locations while brain activity was measured to examine the temporal dynamics of memory-related representations.

Participants

30

Size

76.7 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises magnetoencephalography (MEG) recordings of auditory single word recognition in human subjects. Participants listened to isolated words while neural activity was recorded, providing insights into the temporal dynamics of auditory word comprehension. The dataset is described in Gaston et al. (2022) 'Auditory word comprehension is less incremental in isolated words' published in Neurobiology of Language. The dataset includes raw MEG data, stimulus information, and associated metadata organized according to the Brain Imaging Data Structure (BIDS) standard. This is a NEMAR-converted version of OpenNeuro dataset ds004276.

Participants

19

Citations

1

Size

11.6 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises electroencephalography (EEG) recordings from 21 participants performing a dynamic ship-steering task under four noise conditions: no noise, environmental noise only, controller noise only, and combined environmental and controller noise. Participants controlled a centrally presented rotating arrow to maintain target orientation while experiencing manipulated levels of environmental and controller noise. The task employed unstable dynamics based on the pole-and-cart problem, with performance measured by proximity to target and crash avoidance. This dataset provides rich behavioral and neural data for investigating decision-making, motor control, and adaptation under uncertainty. Note: Participant 12 should be excluded from event-locked analyses due to bad triggers (trigger cable was partially disconnected). The reaction time for the first button press in each round is not recorded in the standard event markers but is available in the participantActions column.

Participants

21

Channels

31 (10-10)

Size

3.30 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset contains EEG recordings from twenty-one participants performing a continuous feedback learning task in which they predicted the final height of an animated rising bar following different predictive cues. The task manipulated outcome predictability (highly predictable, somewhat predictable, unpredictable) using cued gnome stimuli, allowing investigation of neural correlates of continuous feedback and reward prediction processing.

Participants

21

Channels

31 (10-10)

Size

3.48 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset contains EEG recordings from two experiments investigating the cortical encoding of sound source elevation in humans. Subjects were presented with noise bursts from loudspeakers positioned at various elevations in a hemi-anechoic chamber, and were also tested on their ability to localize elevated sound sources. Experiment I used a deviant detection paradigm, while Experiment II used a one-back task involving sound elevation discrimination.

Participants

53

Channels

64 (other)

Size

18.2 GB

Version

v1.0.0

Updated

Aug 19, 2026