DS004521#
Changes in behavioral priority influence the accessibility of working memory content - Experiment 1
Access recordings and metadata through EEGDash.
Citation: Edward Ester, Paige Pytel (2023). Changes in behavioral priority influence the accessibility of working memory content - Experiment 1. 10.18112/openneuro.ds004521.v1.0.1
Modality: eeg Subjects: 34 Recordings: 242 License: CC0 Source: openneuro Citations: 3.0
Metadata: Complete (100%)
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004521
dataset = DS004521(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004521(cache_dir="./data", subject="01")
Advanced query
dataset = DS004521(
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{ds004521,
title = {Changes in behavioral priority influence the accessibility of working memory content - Experiment 1},
author = {Edward Ester and Paige Pytel},
doi = {10.18112/openneuro.ds004521.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds004521.v1.0.1},
}
About This Dataset#
Preprocessed data from Experiment 1 of Ester & Pytel “Changes in behavioral priority influence the accessibility of working memory content”. Analytic scripts for this project can be found on OSF: https://osf.io/gtd5f/. Note that to analyze the BIDS data, you’ll need to modify the analysis scripts to read in the BIDS .set files rather than the expected .mat files. See the OSF wiki for more information
Dataset Information#
Dataset ID |
|
Title |
Changes in behavioral priority influence the accessibility of working memory content - Experiment 1 |
Year |
2023 |
Authors |
Edward Ester, Paige Pytel |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004521,
title = {Changes in behavioral priority influence the accessibility of working memory content - Experiment 1},
author = {Edward Ester and Paige Pytel},
doi = {10.18112/openneuro.ds004521.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds004521.v1.0.1},
}
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!
Technical Details#
Subjects: 34
Recordings: 242
Tasks: 1
Channels: 62
Sampling rate (Hz): 250.0
Duration (hours): 0.0
Pathology: Not specified
Modality: —
Type: —
Size on disk: 10.7 GB
File count: 242
Format: BIDS
License: CC0
DOI: doi:10.18112/openneuro.ds004521.v1.0.1
API Reference#
Use the DS004521 class to access this dataset programmatically.
- class eegdash.dataset.DS004521(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Bases:
EEGDashDatasetOpenNeuro dataset
ds004521. Modality:eeg; Experiment type:Motor. Subjects: 34; recordings: 34; tasks: 1.- 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.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/ds004521 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004521
Examples
>>> from eegdash.dataset import DS004521 >>> dataset = DS004521(cache_dir="./data") >>> recording = dataset[0] >>> raw = recording.load()
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset