DS004317#

Mood Manipulation and PST, Experiment 2

Access recordings and metadata through EEGDash.

Citation: James F Cavanagh, Trevor C J Jackson (2022). Mood Manipulation and PST, Experiment 2. 10.18112/openneuro.ds004317.v1.0.3

Modality: eeg Subjects: 50 Recordings: 425 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004317

dataset = DS004317(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 = DS004317(cache_dir="./data", subject="01")

Advanced query

dataset = DS004317(
    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{ds004317,
  title = {Mood Manipulation and PST, Experiment 2},
  author = {James F Cavanagh and Trevor C J Jackson},
  doi = {10.18112/openneuro.ds004317.v1.0.3},
  url = {https://doi.org/10.18112/openneuro.ds004317.v1.0.3},
}

About This Dataset#

Reinforcement learning task with 50 healthy controls (25 after a sad mood manipulation, 25 after a happy mood manipulation) Task with a training section and testing section. Task adapted from here: https://doi.org/10.1126/science.1102941. Mood Manipulation occurs during task before each training block. Task included in Matlab programming language. Data collected from 2019-2021 in Cognitive Rhythms and Computation Lab at University of New Mexico. Check the .xls sheet under code folder for more meta data. - Trevor CJ Jackson 10/27/2022

Dataset Information#

Dataset ID	`DS004317`
Title	Mood Manipulation and PST, Experiment 2
Year	2022
Authors	James F Cavanagh, Trevor C J Jackson
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004317.v1.0.3
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: 50
Recordings: 425
Tasks: 1

Channels & sampling rate

Channels: 60 (50), 66 (50)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS004317 class to access this dataset programmatically.

class eegdash.dataset.DS004317(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004317. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 50; recordings: 50; 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/ds004317 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004317

Examples

>>> from eegdash.dataset import DS004317
>>> dataset = DS004317(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