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# README This dataset is a BIDS compatible version of the SeizeIT2 dataset. It reorganizes the file structure to comply with the BIDS specification. To this effect: - Metadata was organized according to BIDS. - Data in the edf files contains wearable EEG, ECG, EMG and movement data recorded with the Sensor Dot device. - Annotations were formatted as BIDS-score compatible `tsv` files. ### Contact person The dataset was published by [Miguel Bhagubai]([email protected]) and [Chri

Participants

125

Size

44.4 GB

Updated

Jun 6, 2026

This is a BIDS standardized version of simultaneously collected EEG and eye-tracking data, taken from one subject from the [EEGEYENET](https://osf.io/ktv7m/) dataset. Acknowledgements go to Martyna Beata Płomecka, Ard Kastrati, and Nicolas Langer who designed the study, collected the data, and published the dataset to Open Science Framework. For access to the full dataset, please refer to the dataset DOI.

Participants

1

Size

39.9 MB

Updated

Jun 6, 2026

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

Participants

48

Size

2.93 GB

Updated

Jun 6, 2026

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

Participants

60

Size

3.57 GB

Updated

Jun 6, 2026

## The “Cognitive Electrophysiology in Socioeconomic Context in Adulthood” Dataset # Data Description This dataset comprises electroencephalogram (EEG) data collected from 127 young adults (18-30 years), along with retrospective objective and subjective indicators of childhood family socioeconomic status (SES), as well as SES indicators in adulthood, such as educational attainment, individual and household income, food security, and home and neighborhood characteristics. The EEG data were recor

Participants

127

Size

10.6 GB

Updated

Jun 6, 2026

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., De

Participants

33

Size

284 GB

Updated

Jun 6, 2026

# EEG Experiment Measuring ERPs in VR This dataset contains EEG recordings from a study investigating event-related potentials (ERPs) during different visual tasks in virtual reality. ## Study Design - **Participants**: 48 participants - **Tasks**: - Lumfront - Lumperp - Regfront - Regperp - Signalscreen - Signalvr - **Modality**: EEG (512 Hz sampling rate) ## Dataset Organization The dataset follows the BIDS specification (version 1.6.0). Each subject folder contains EEG recordi

Participants

48

Size

7.35 GB

Updated

Jun 6, 2026

# Summary The human brain can rapidly recognize meaningful objects from natural scenes encountered in everyday life. Neuroimaging with large-scale naturalistic stimuli is increasingly employed to elucidate these neural mechanisms of object recognition across these rich and daily natural scenes. However, most existing large-scale neuroimaging datasets with naturalistic stimuli primarily rely on functional magnetic resonance imaging (fMRI), which provides high spatial resolution to characterize s

Participants

19

Size

14.1 GB

Updated

Jun 6, 2026

# Summary The human brain can rapidly recognize meaningful objects from natural scenes encountered in everyday life. Neuroimaging with large-scale naturalistic stimuli is increasingly employed to elucidate these neural mechanisms of object recognition across these rich and daily natural scenes. However, most existing large-scale neuroimaging datasets with naturalistic stimuli primarily rely on functional magnetic resonance imaging (fMRI), which provides high spatial resolution to characterize s

Participants

31

Size

147 GB

Updated

Jun 6, 2026

Overview The study comprises data of a combined fMRI/EEG experiment. The EEG files contain 63 head channels, ECG, EOG, facial EMG and skin conductance data. A physio file contains respiration and finger-pulse data. In addition, a T1 weighted whole-brain anatomical MR scan, a PD weighted (UTE) scan for electrode localization is provided (defacing was performed using https://github.com/cbinyu/pydeface). Additional data of the participants (T2 weighted images, button press dynamics, hearing thresh

Participants

34

Size

6.41 GB

Updated

Jun 6, 2026