DS006446#

Cueing the future to reduce temporal discounting

Access recordings and metadata through EEGDash.

Citation: Isaac Kinley, Sue Becker (2025). Cueing the future to reduce temporal discounting. 10.18112/openneuro.ds006446.v1.0.0

Modality: eeg Subjects: 29 Recordings: 179 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006446

dataset = DS006446(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 = DS006446(cache_dir="./data", subject="01")

Advanced query

dataset = DS006446(
    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{ds006446,
  title = {Cueing the future to reduce temporal discounting},
  author = {Isaac Kinley and Sue Becker},
  doi = {10.18112/openneuro.ds006446.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006446.v1.0.0},
}

About This Dataset#

EEG study of episodic future thinking and delay discounting, to be described in a forthcoming paper. Briefly, participants described a series of future events and were then cued to think about these events as they made intertemporal choices. They were also asked how vivid their mental imagery of these events was.

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., 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	`DS006446`
Title	Cueing the future to reduce temporal discounting
Year	2025
Authors	Isaac Kinley, Sue Becker
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006446.v1.0.0
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: 29
Recordings: 179
Tasks: 1

Channels & sampling rate

Channels: 64 (29), 65 (29)
Sampling rate (Hz): 2048.0
Duration (hours): 0.0

API Reference#

Use the DS006446 class to access this dataset programmatically.

class eegdash.dataset.DS006446(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006446. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 29; recordings: 29; 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. 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/ds006446 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006446

Examples

>>> from eegdash.dataset import DS006446
>>> dataset = DS006446(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