DS004502#

Anticipatory differences between Attention and Expectation

Access recordings and metadata through EEGDash.

Citation: Jose M. G. Penalver, David Lopez-Garcia, Blanca Aguado-Lopez, Carlos Gonzalez-Garcia, Maria Ruz (2023). Anticipatory differences between Attention and Expectation. 10.18112/openneuro.ds004502.v1.0.1

Modality: eeg Subjects: 48 Recordings: 319 License: CC0 Source: openneuro Citations: 3.0

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004502

dataset = DS004502(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 = DS004502(cache_dir="./data", subject="01")

Advanced query

dataset = DS004502(
    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{ds004502,
  title = {Anticipatory differences between Attention and Expectation},
  author = {Jose M. G. Penalver and David Lopez-Garcia and Blanca Aguado-Lopez and Carlos Gonzalez-Garcia and Maria Ruz},
  doi = {10.18112/openneuro.ds004502.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004502.v1.0.1},
}

About This Dataset#

No README content is available for this dataset.

Dataset Information#

Dataset ID	`DS004502`
Title	Anticipatory differences between Attention and Expectation
Year	2023
Authors	Jose M. G. Penalver, David Lopez-Garcia, Blanca Aguado-Lopez, Carlos Gonzalez-Garcia, Maria Ruz
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004502.v1.0.1
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: 48
Recordings: 319
Tasks: 1

Channels & sampling rate

Channels: 63 (88), 65 (8)
Sampling rate (Hz): 1000.0 (88), 500.0 (8)
Duration (hours): 0.0

API Reference#

Use the DS004502 class to access this dataset programmatically.

class eegdash.dataset.DS004502(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004502. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 48; recordings: 48; 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/ds004502 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004502

Examples

>>> from eegdash.dataset import DS004502
>>> dataset = DS004502(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