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VR-CCT Dataset Compassion Island was a social world implemented in AltspaceVR by tecchnology collaborators Origami Air. It was specifically created for the study of VR-based augmentation of compassion cultivation training (CCT). It featured three main settings – a meditation hall, a garden courtyard with a large willow tree, and a clinic. During experimental sessions, participants interacted with two characters in these spaces, represented as

Participants

10

Size

919 MB

This dataset accompanies the paper “Motor Control Processes Moderate Working Memory Gating,” published in The Journal of Neuroscience. It contains the raw EEG recordings (not preprocessed) from the study, as well as each participant’s behavioral data within the EEG dataset struct (labeled as EEG.behaviordata). Mean age of subjects is 23.7 (sd=2.9). For any of your inquiries, please reach out to the corresponding author: oezdemir@ifado.de You can find the explanation of triggers at"task-V

Participants

32

Size

52.8 GB

EEG-Controlled Exoskeleton for Walking and Standing A Longitudinal Motor Imagery Study in Healthy Adults Dataset Overview This dataset contains multimodal recordings from a brain–machine interface (BMI) training study involving seven healthy adult participants (ages 20–30, Mean = 24.3, SD = 3.8). The study focused on open-loop and closed-loop control of a lower-limb exoskeleton (Rex Bionics) using EEG and inertial sensor data. Each participant completed nine sessions over several weeks, structu

Participants

7

Size

3.62 GB

Neuroimaging Data Collected During Kinesthetic Motor Imagery of Walking vs. Rest ------------------------------------------------------------------------------- This dataset includes multimodal neuroimaging recordings from five participants performing kinesthetic motor imagery (KI) while viewing themselves walking in an exoskeleton. The recordings combine MRI (structural and functional) and EEG data, organized according to the BIDS specification. MRI sessions were conducted after participants c

Participants

5

Size

5.37 GB

References ---------- Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C.

Participants

15

Size

5.99 GB

The data set contains anonymized raw magnetoencephalography (MEG) recordings of 23 healthy adult participants, performed at Neurospin, Gif sur Yvette, France. Participants performed an n-item delayed temporal reproduction task: They were presented with a sequence of one or three “empty” intervals (see cover figure), delimited by short pure tones. They had to maintain the sequence in memory (retention), and, upon a prompt, reproduce the whole sequence by pressing a button for each tone. Eight tas

Participants

24

Size

136 GB

This dataset, used in the analysis reported by Kochi et al., (2025), contains intracranial EEG recordings from 110 individuals who performed an visual‑naming task. Electrode coordinates are provided in MNI‑305 space. Each EDF file is tagged for the visual naming task with the following event codes: 401 – stimulus onset 501 – response onset Reference: Ryuzaburo Kochi, Aya Kanno, Hiroshi Uda, Keisuke Hatano, Masaki Sonoda, Hidenori Endo, Michael Cools, Robert Rothermel, Aimee F. Luat, Ei

Participants

110

Size

17.5 GB

This dataset, used in the analysis reported by Kochi et al., (2025), contains intracranial EEG recordings from 121 individuals who performed an auditory‑naming task. Electrode coordinates are provided in MNI‑305 space. Each EDF file is tagged for the auditory naming task with the following event codes: 401 – stimulus onset 402 – stimulus offset 501 – response onset Reference: Ryuzaburo Kochi, Aya Kanno, Hiroshi Uda, Keisuke Hatano, Masaki Sonoda, Hidenori Endo, Michael Cools, Robert

Participants

121

Size

44.6 GB

# Dataset of Electroencephalograms of Juvenile Offenders ## Project's name Desarrollo de un sistema inteligente multiparamétrico para el reconocimiento de patrones asociados a disfunciones neurocognitivas en jóvenes en conflicto con la ley en el departamento del Atlántico. ## Year of project execution 2021 ## Authors and acknowledgment Aura Polo, Elmer León, Mariana Pino-Melgarejo and Julie Viloria-Porto. Ronald Ruiz for his assistance during the data collection process, and

Participants

140

Size

8.09 GB

Data collection took place at the NeuroCognition Laboratory (NCL) in San Diego, California under the supervision of Dr. Phillip Holcomb. This project followed the San Diego State University’s IRB guidelines.

Participants

29

Size

2.28 GB