Discover datasets

This dataset comprises electroencephalography (EEG) recordings from 7 participants performing an auditory imagery task, wherein subjects imagined sounds produced by objects from semantic categories (animals and tools) for 5-second intervals. EEG signals were acquired using a 64-channel BioSemi ActiveTwo system sampled at 2048 Hz, with concurrent electrooculography and physiological monitoring. The dataset supports research in semantic decoding and brain-computer interface applications using imagined auditory stimuli.

Participants

7

Channels

64 (10-10)

Size

12.7 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises electroencephalography (EEG) recordings from 54 community participants (high and low alcohol drinkers) performing an affective state reinforcement learning task with alcohol imagery. Data were collected between 2019-2021 at the University of New Mexico's Clinical Research Center Lab. The study investigates how alcohol imagery modulates reward-related neural responses, as indexed by the reward positivity component, in individuals with varying drinking patterns.

Participants

54

Channels

66 (10-10)

Size

9.45 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises simultaneous EEG and fNIRS recordings from 12 participants performing semantic imagery tasks involving silent naming and sensory-based imagination of animals and tools. Participants engaged in visual, auditory, and tactile perception tasks while neural activity was captured using a 64-channel BioSemi EEG system and a NIRx fNIRS imaging system with integrated optodes. The multimodal neuroimaging data supports research in semantic decoding and brain-computer interface applications.

Participants

12

Channels

64 (biosemi)

Size

24.1 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises electroencephalography, physiological, and behavioral data collected from 44 healthy individuals (ages 21-40) during resting-state and task-based conditions. Participants completed a spontaneous deception task (Gambling Game) and cognitive control assessments involving processing speed, response selection, response inhibition, and conflict resolution. The dataset includes concurrent EEG recordings at 3000 Hz and multimodal physiological signals (ECG, respiration, electrodermal activity, electromyography) at 4000 Hz, providing a comprehensive neurophysiological characterization of deception and cognitive control processes. This dataset is derived from and reformatted from the OpenNeuro source dataset (doi:10.18112/openneuro.ds004511.v1.0.2) into BIDS format for the NEMAR repository.

Participants

45

Channels

139 (other)

Size

202 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises multimodal neurophysiological and kinematic recordings from 25 participants engaged in real-world table tennis gameplay. The collection includes high-density dual-layer electroencephalography, cervical electromyography, inertial measurement unit acceleration data, structural MRI, and synchronized video recordings. Participants completed 60 minutes of table tennis play against both a ball machine and human opponent, with 10 minutes of standing baseline recording. Processed data include independent component analysis decomposition and dipole modeling for EEG signals.

Participants

25

Channels

120 (other)

Citations

24

Size

523 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset investigates the neural mechanisms underlying anticipatory processing by comparing attention and expectation effects during a cognitive task. The study employs neuroimaging to dissociate how top-down attentional control and predictive expectation modulate brain activity in preparation for upcoming stimuli, providing insights into fundamental mechanisms of anticipatory cognition.

Participants

48

Channels

65 (10-10)

Size

59.4 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset contains magnetoencephalography (MEG) recordings from participants listening to hierarchically structured binary sound sequences of varying complexity. The study tests the language of thought hypothesis by examining how the human brain compresses regular sequences in working memory using recursive structures. Brain activity was recorded while participants processed sequences requiring different levels of complexity (transition probabilities, chunking, or nested structures) and responded to occasional deviant sounds probing sequence knowledge. Note: This dataset contains MEG data only; fMRI data from the same study are archived separately.

Participants

19

Size

23.4 GB

Version

vv1.0.0

Updated

Jul 10, 2026

The Pandemic Emergency Scenario (PES) dataset comprises neurophysiological and behavioral data from 9 participants engaged in a complex resource allocation task during a simulated pandemic emergency. Participants, paired with an artificial agent, made sequential decisions about allocating limited medical resources across affected cities while reporting confidence in their choices. EEG recordings, electrocardiography, and electrooculography were simultaneously acquired to characterize neural correlates of decision-making under resource constraints.

Participants

9

Channels

79 (10-10)

Size

22.3 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises stereoelectroencephalography (sEEG) recordings from patients performing a forced two-choice response task, collected in the epilepsy monitoring unit at Oregon Health & Science University. The data extends characterization of movement-related neural oscillations using intracranial electrode recordings, providing insights into canonical motor-related activity patterns across distributed brain regions.

Participants

8

Size

14.2 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises simultaneous EEG and motion capture recordings from 20 healthy adults performing a spatial orientation task involving full-body heading changes. Participants completed a spot rotation task under two conditions: joystick-controlled visual rotation on a 2D display and physical full-body rotation in a virtual reality environment. The dataset includes 157-channel EEG data (129 scalp + 28 neck electrodes) sampled at 1000 Hz and multi-camera motion capture data at 90 Hz, enabling investigation of cortical dynamics and sensorimotor integration during naturalistic spatial navigation.

Participants

20

Channels

160 (other)

Size

61.3 GB

Version

vv1.0.0

Updated

Jul 10, 2026