DS004816#

EEG-attention-rsvp-exp1

Access recordings and metadata through EEGDash.

Citation: Grootswagers, Tijl, Robinson, Amanda, Shatek, Sofia, Carlson, Thomas (2023). EEG-attention-rsvp-exp1. 10.18112/openneuro.ds004816.v1.0.1

Modality: eeg Subjects: 20 Recordings: 84 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004816

dataset = DS004816(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 = DS004816(cache_dir="./data", subject="01")

Advanced query

dataset = DS004816(
    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{ds004816,
  title = {EEG-attention-rsvp-exp1},
  author = {Grootswagers, Tijl and Robinson, Amanda and Shatek, Sofia and Carlson, Thomas},
  doi = {10.18112/openneuro.ds004816.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004816.v1.0.1},
}

About This Dataset#

EEG data for Grootswagers et al 2021 experiment 1 (small letters on big objects)

Grootswagers T., Robinson A.K., Shatek S.M., Carlson T.A. (2021). The neural dynamics underlying prioritisation of task-relevant information. Neurons, Behaviour, Data Analysis, and Theory, 5(1) https://doi.org/10.51628/001c.19129

Dataset Information#

Dataset ID	`DS004816`
Title	EEG-attention-rsvp-exp1
Year	2023
Authors	Grootswagers, Tijl, Robinson, Amanda, Shatek, Sofia, Carlson, Thomas
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004816.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: 20
Recordings: 84
Tasks: 1

Channels & sampling rate

Channels: 63
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS004816 class to access this dataset programmatically.

class eegdash.dataset.DS004816(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004816. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 20; recordings: 20; 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/ds004816 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004816

Examples

>>> from eegdash.dataset import DS004816
>>> dataset = DS004816(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