DS003766: eeg dataset, 31 subjects#
A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking
Citation: Kun Chen, Ruien Wang, Jiamin Huang, Fei Gao, Zhen Yuan, Yanyan Qi, Haiyan Wu (2022). A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking. 10.18112/openneuro.ds003766.v2.0.3
31-participant EEG dataset — A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003766
dataset = DS003766(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003766(cache_dir="./data", subject="01")
Advanced query
dataset = DS003766(
cache_dir="./data",
query={"subject": {"$in": ["01", "02"]}},
)
Iterate recordings
for rec in dataset:
print(rec.subject, rec.raw.info['sfreq'])
If you use this dataset in your research, please cite the original authors.
BibTeX
@dataset{ds003766,
title = {A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking},
author = {Kun Chen and Ruien Wang and Jiamin Huang and Fei Gao and Zhen Yuan and Yanyan Qi and Haiyan Wu},
doi = {10.18112/openneuro.ds003766.v2.0.3},
url = {https://doi.org/10.18112/openneuro.ds003766.v2.0.3},
}
About This Dataset#
This dataset was collected in 2020, which combines high-density Electroencephalography (HD-EEG, 128 channels) and mouse-tracking intended as a resource for examining the dynamic decision process of semantics and preference choices in the human brain. The dataset includes high-density resting-state and task-related (food preference choices and semantic judgments) EEG acquired from 31 individuals (ages: 18-33).
The EEG data were acquired using a 128-channel cap based on the standard 10/20 System with Electrical Geodesics Inc (EGI, Eugene, Oregon) system. During recording, sampling rate was 1000Hz, and the E129 (Cz) electrode was used as reference. Electrode impedances were kept below 50kohm for each electrode during the experiment.
A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking
Description
Main files
``sub-*``: EEG (.set) and behavior data with BIDS format.
``sourcedata/rawdata``: Raw .mff EGI data and behavior data with subject information desensitization.
``sourcedata/psychopy``: Stimuli and PsychoPy scripts for presentation.
``derivatives/eeglab-preproc``: Preprocessed continuous EEG data with EEGLAB (Easy to set different epoch time windows for further analysis).
Others
Please refer to the corresponding paper and GitHub code to get more details.
References
Chen, K., Wang, R., Huang, J., Gao, F., Yuan, Z., Qi, Y., & Wu, H. (2022). A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking. Scientific Data, 9(1), 416. https://doi.org/10.1038/s41597-022-01538-5 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., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8
Cohort#
Dataset Statistics#
Age distribution by gender (n=31, range 18–33 yr, mean 20.7 yr)
Sex composition
Channel counts: 129 ch (n=124 recordings)
Sampling frequencies: 1000.0 Hz (n=124 recordings)
Total recording duration: 40 h
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-foodchoice
Showing one representative recording out of
31 subjects and 124 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 129 sensors — 129 channels
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2022 |
Authors |
Kun Chen, Ruien Wang, Jiamin Huang, Fei Gao, Zhen Yuan, Yanyan Qi, Haiyan Wu |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003766,
title = {A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking},
author = {Kun Chen and Ruien Wang and Jiamin Huang and Fei Gao and Zhen Yuan and Yanyan Qi and Haiyan Wu},
doi = {10.18112/openneuro.ds003766.v2.0.3},
url = {https://doi.org/10.18112/openneuro.ds003766.v2.0.3},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003766 · Chen2021eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003766(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking
- Study:
ds003766(OpenNeuro)- Author (year):
Chen2021- Canonical:
—
Also importable as:
DS003766,Chen2021.Modality:
eeg. Subjects: 31; recordings: 124; tasks: 4.- Parameters:
cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key
dataset.s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to
EEGDashDataset.
- data_dir#
Local dataset cache directory (
cache_dir / dataset_id).- Type:
Path
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds003766 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003766 DOI: https://doi.org/10.18112/openneuro.ds003766.v2.0.3 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS003766 >>> dataset = DS003766(cache_dir="./data") >>> recording = dataset[0] >>> raw = recording.load()
- __init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
- save(path: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds003766").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds003766 to reproduce the tutorial on this dataset.
Citation
Kun Chen, Ruien Wang, Jiamin Huang, Fei Gao, Zhen Yuan, … (2022). A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking. 10.18112/openneuro.ds003766.v2.0.3
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003766.v2.0.3.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset