DS004262#

Continuous Feedback Processing

Access recordings and metadata through EEGDash.

Citation: Cameron D. Hassall, Yan Yan, Laurence T. Hunt (2022). Continuous Feedback Processing. 10.18112/openneuro.ds004262.v1.0.0

Modality: eeg Subjects: 21 Recordings: 194 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004262

dataset = DS004262(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004262(cache_dir="./data", subject="01")

Advanced query

dataset = DS004262(
    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{ds004262,
  title = {Continuous Feedback Processing},
  author = {Cameron D. Hassall and Yan Yan and Laurence T. Hunt},
  doi = {10.18112/openneuro.ds004262.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004262.v1.0.0},
}

About This Dataset#

Continuous Feedback Processing

Twenty-one participants learned to predict the final level of an animated rising bar. Following the appearance of a fixation cross, participants used the mouse to indicate their guess (i.e., how high they thought the bar would rise). After a delay, participants watched the bar rise to its final level. Points were awarded based on the distance between their guess and the actual level. Each round was cued by the appearance of a gnome (cover story: the gnomes are playing a strongman game while visiting a fair). Cues varied in the degree to which the outcome was predictable (highly predictable, somewhat predictable, unpredictable).

Participant 11 was excluded from the analysis due to excessive artifacts.

Timing fixation cross (400-600 ms) -> gnome cue (1500 ms) -> bar outline (until response) -> animation (1 degree per second until complete) -> final outcome (1000 ms)

Conditions (Gnome Types) 1: highly predictable - consistently low 2: highly predictable - consistently high 3: unpredictable - low or high with equal probability 4: somewhat predictable - usually (80%) low, sometimes high 5: somewhat predictable - usually (80%) high, sometimes low 6: unpredictable - random uniform distribution

Trigger Modifiers Add 0: Fixation cross Add 10: Cue (gnome) onset Add 20: Bar outline appears Add 30: Participant response Add 40: Start of animation Add 50: End of animation (and start of 1-second delay)

Dataset Information#

Dataset ID

DS004262

Title

Continuous Feedback Processing

Year

2022

Authors

Cameron D. Hassall, Yan Yan, Laurence T. Hunt

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004262.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004262,
  title = {Continuous Feedback Processing},
  author = {Cameron D. Hassall and Yan Yan and Laurence T. Hunt},
  doi = {10.18112/openneuro.ds004262.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004262.v1.0.0},
}

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: 21

  • Recordings: 194

  • Tasks: 1

Channels & sampling rate

  • Channels: 31

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 3.5 GB

  • File count: 194

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004262.v1.0.0

Provenance

API Reference#

Use the DS004262 class to access this dataset programmatically.

class eegdash.dataset.DS004262(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004262. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 21; recordings: 21; 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/ds004262 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004262

Examples

>>> from eegdash.dataset import DS004262
>>> dataset = DS004262(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

See Also#