DS003352#

1 - Light Pink Spiral

Access recordings and metadata through EEGDash.

Citation: Katherine Hermann, Isabelle Rosenthal, Shridhar R. Singh, Dimitrios Pantazis, Bevil R. Conway (2020). 1 - Light Pink Spiral. 10.18112/openneuro.ds003352.v1.0.0

Modality: meg Subjects: 18 Recordings: 629 License: CC0 Source: openneuro Citations: 4.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003352

dataset = DS003352(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 = DS003352(cache_dir="./data", subject="01")

Advanced query

dataset = DS003352(
    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{ds003352,
  title = {1 - Light Pink Spiral},
  author = {Katherine Hermann and Isabelle Rosenthal and Shridhar R. Singh and Dimitrios Pantazis and Bevil R. Conway},
  doi = {10.18112/openneuro.ds003352.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds003352.v1.0.0},
}

About This Dataset#

Stimuli include eight different square wave spiral gratings subtending 10 degrees of visual angle as well as the color words “blue” and “green.” The color words appeared as white on a gray background. Each stimulus appeared on the screen for 116 ms. The triggers or event ID’s of each stimulus are as follows:

1 - Light Pink Spiral 2 - Dark Pink Spiral 3 - Light Blue Spiral 4 - Dark Blue Spiral 5 - Light Green Spiral 6 - Dark Green Spiral 7 - Light Orange Spiral 8 - Dark Orange Spiral 9 - “green” 10 - “blue”

Dataset Information#

Dataset ID	`DS003352`
Title	1 - Light Pink Spiral
Year	2020
Authors	Katherine Hermann, Isabelle Rosenthal, Shridhar R. Singh, Dimitrios Pantazis, Bevil R. Conway
License	CC0
Citation / DOI	10.18112/openneuro.ds003352.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: 18
Recordings: 629
Tasks: 1

Channels & sampling rate

Channels: 323 (138), 306 (138)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS003352 class to access this dataset programmatically.

class eegdash.dataset.DS003352(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003352. Modality: meg; Experiment type: Perception; Subject type: Unknown. Subjects: 18; recordings: 138; 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/ds003352 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003352

Examples

>>> from eegdash.dataset import DS003352
>>> dataset = DS003352(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