DS004147#

Average Task Value

Access recordings and metadata through EEGDash.

Citation: Cameron D. Hassall, Laurence T. Hunt, Clay B. Holroyd (2022). Average Task Value. 10.18112/openneuro.ds004147.v1.0.2

Modality: eeg Subjects: 12 Recordings: 113 License: CC0 Source: openneuro Citations: 2.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004147

dataset = DS004147(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 = DS004147(cache_dir="./data", subject="01")

Advanced query

dataset = DS004147(
    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{ds004147,
  title = {Average Task Value},
  author = {Cameron D. Hassall and Laurence T. Hunt and Clay B. Holroyd},
  doi = {10.18112/openneuro.ds004147.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004147.v1.0.2},
}

About This Dataset#

Average Task Value

Twelve participants completed three learning tasks. In each task the goal was to learn cue-response mappings for six cues. The cues were various coloured shapes. The possible responses were left (‘d’ key) or right (‘k’ key). There were two types of cues. Low-value cues had a feedback validity of 0.5 (i.e., a coin toss). High-value cues had a feedback validity of 0.8 (80% chance of a win if the correct action was chosen). The low-value task contained only low-value cues. The high-value task contained only high-value cues. The mid-value task contained three low-value cues and three high-value cues. Participants completed 144 trials of each task.

Preprint: https://doi.org/10.1101/2021.09.16.460600

Analysis code: chassall/averagetaskvalue

Dataset Information#

Dataset ID	`DS004147`
Title	Average Task Value
Year	2022
Authors	Cameron D. Hassall, Laurence T. Hunt, Clay B. Holroyd
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004147.v1.0.2
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: 12
Recordings: 113
Tasks: 1

Channels & sampling rate

Channels: 31
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS004147 class to access this dataset programmatically.

class eegdash.dataset.DS004147(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004147. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 12; recordings: 12; 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/ds004147 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004147

Examples

>>> from eegdash.dataset import DS004147
>>> dataset = DS004147(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