DS003766#

A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking

Access recordings and metadata through EEGDash.

Citation: Kun Chen, Ruien Wang, Jiamin Huang, Fei Gao, Zhen Yuan, Yanyan Qi, Haiyan Wu (2021). A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking. 10.18112/openneuro.ds003766.v2.0.3

Modality: eeg Subjects: 31 Recordings: 124 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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#

A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking

Description

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).

EEG acquisition

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.

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

Dataset Information#

Dataset ID	`DS003766`
Title	A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking
Year	2021
Authors	Kun Chen, Ruien Wang, Jiamin Huang, Fei Gao, Zhen Yuan, Yanyan Qi, Haiyan Wu
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003766.v2.0.3
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 31
Recordings: 124
Tasks: 4

Channels & sampling rate

Channels: 129
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS003766 class to access this dataset programmatically.

class eegdash.dataset.DS003766(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003766. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. 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

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and 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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None