DS003822#

EEG: Probabilistic Learning with Affective Feedback: Exp #1

Access recordings and metadata through EEGDash.

Citation: Darin R. Brown, Trevor Jackson, James F Cavanagh (2021). EEG: Probabilistic Learning with Affective Feedback: Exp #1. 10.18112/openneuro.ds003822.v1.1.0

Modality: eeg Subjects: 25 Recordings: 259 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003822

dataset = DS003822(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 = DS003822(cache_dir="./data", subject="01")

Advanced query

dataset = DS003822(
    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{ds003822,
  title = {EEG: Probabilistic Learning with Affective Feedback: Exp #1},
  author = {Darin R. Brown and Trevor Jackson and James F Cavanagh},
  doi = {10.18112/openneuro.ds003822.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds003822.v1.1.0},
}

About This Dataset#

RL task in N=25 college age participants. Data collected circa 2018 in the CRCL at UNM. The paper [Brown, D.R., Jackson, T.J. & Cavanagh, J.F. The Reward Positivity is sensitive to affective liking] is now coming out in Cognitive, Affective, & Behavioral Neuroscience. Your best bet for understanding this task would be to read that paper first. I’ve included additional scripts to help understand stimulus triggers etc. These additional scripts were for a secondary analysis: they were not the scripts used for the paper above. So they are slightly different and have some interesting (unfinished) tangents. - James F Cavanagh 09/29/2021

Dataset Information#

Dataset ID	`DS003822`
Title	EEG: Probabilistic Learning with Affective Feedback: Exp #1
Year	2021
Authors	Darin R. Brown, Trevor Jackson, James F Cavanagh
License	CC0
Citation / DOI	10.18112/openneuro.ds003822.v1.1.0
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: 25
Recordings: 259
Tasks: 1

Channels & sampling rate

Channels: 66 (25), 64 (25)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS003822 class to access this dataset programmatically.

class eegdash.dataset.DS003822(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003822. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 25; recordings: 25; 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/ds003822 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003822

Examples

>>> from eegdash.dataset import DS003822
>>> dataset = DS003822(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