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# UCSD Forehead Patch Sleep Validation Dataset Curated EEG recordings for validating sleep staging from a three-electrode forehead patch against standard 33-channel polysomnography (not included in this release -- this release only includes the three-electrode data). ## What is included - CGX patch `.set` files that contain `EEG.VisualHypnogram` and `EEG.SpectralScoring` ## Sleep stage labels `EEG.VisualHypnogram` is manual scoring in 30-second epochs using the following integers 1 equals Wa

Participants

19

Size

9.36 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., D

Participants

36

Size

31.1 GB

# README ## Contact For additional information, please contact: Ethan R. Buch (ethan.buch@nih.gov; ORCID: 0000-0002-5443-8222) ## Overview This study was carried out by the Human Cortical Physiology & Neurorehabilitation Section (HCPS) in the NINDS Intramural Research Program. The primary aim of the study was to investigate changes in brain activity associated with early skill learning during an initial training session, overnight skill consolidation and longer-term skill retention. A long

Participants

31

Size

95.8 GB

Overview -------- This is the dataset for our study investigating the effects of selective attention to speech stimuli in the subcortex and cortex, entitled "The auditory brainstem response to natural speech is not affected by selective attention" by Stoll et al. (2025). Please cite our paper if you use our dataset. It contains EEG data for three experiments, detailed in the paper and briefly summarized below. Code and stimuli to derive the responses are provided in the Dataset folder and o

Participants

66

Size

103 GB

# Multimodal Sensor Fusion for EEG-Based BCI Typing Systems ## Dataset Overview This dataset contains recordings of EEG and EyeTracking for a BCI spelling task. The data were collected in 2023 at Northeastern University. - N=21 - Calibration task were proctored using BciPy [1] - The dataset is organized in accordance with the Brain Imaging Data Structure (BIDS) specification (version 1.7.0). ## Methodology Calibration data were collected from control participants (n=21, mean age 23.6 ± 3.1

Participants

21

Size

442 MB

## Overview This dataset was originally published in Gramann, K., Töllner, T. and Müller, H.J. (2010), Dimension-based attention modulates early visual processing. Psychophysiology, 47: 968-978. https://doi.org/10.1111/j.1469-8986.2010.00998.x It was subsequently used to investigate automatic labeling of independent components in ICA and is referred to as the "Cue" dataset: Frølich, L., Andersen, T.S. and Mørup, M. (2015), Multi-class classification of ICS of EEG. Psychophysiol, 52: 32-45. https

Participants

12

Size

5.63 GB

# README # WARNING Below is a template to write a README file for this BIDS dataset. If this message is still present, it means that the person exporting the file has decided not to update the template.If you are the researcher editing this README file, please remove this warning section. The README is usually the starting point for researchers using your dataand serves as a guidepost for users of your data. A clear and informativeREADME makes your data much more usable. In general you can inclu

Participants

47

Size

12.1 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 Niso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A

Participants

24

Size

102 GB

This dataset contains high-density EEG recordings collected during a visual stimulation task. Files are organized according to the EEG-BIDS specification. Raw data are BrainVision (.vhdr/.vmrk/.eeg). Task: visual Session: ses-01 Provenance: Converted from c:/shared/raw_eeg with this helper script. No acquisition-time filters applied (offline preprocessing not included here).

Participants

31

Size

17.6 GB

Citation Toffolo, K.K., Freedman, E.G., Foxe, J.J. Neurophysiological measures of covert semantic processing in neurotypical adolescents actively ignoring spoken sentence inputs: A high-density event-related potential (ERP) study. Neuroscience. 560:238-253 (2024). PMID PMC39369943 DOI: 10.1016/j.neuroscience. 2024.10.008 Project name and executive summary Title: Neurophysiological measures of covert semantic processing in neurotypical adolescents actively ignoring spoken sentence inputs: A h