DS004369#

Blink-Pause-Relation (Competing Speaker Paradigm)

Access recordings and metadata through EEGDash.

Citation: Bjoern Holtze, Marc Rosenkranz, Martin Bleichner, Stefan Debener (2022). Blink-Pause-Relation (Competing Speaker Paradigm). 10.18112/openneuro.ds004369.v1.0.1

Modality: eeg Subjects: 41 Recordings: 345 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004369

dataset = DS004369(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 = DS004369(cache_dir="./data", subject="01")

Advanced query

dataset = DS004369(
    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{ds004369,
  title = {Blink-Pause-Relation (Competing Speaker Paradigm)},
  author = {Bjoern Holtze and Marc Rosenkranz and Martin Bleichner and Stefan Debener},
  doi = {10.18112/openneuro.ds004369.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004369.v1.0.1},
}

About This Dataset#

Within this study the EOG channels from two previous studies were pooled. 20 participants from Jaeger et al. (2020) and 21 from Holtze et al. (2021) were included. Participants performed a two-competing speaker paradigm in both original studies. Participants were instructed to either attend to the left or right audio book. The paradigm consisted of six (Jaeger et al. 2020) or five (Holtze et al. 2021) 10-minute blocks of audio book presentation. In Jaeger et al. (2020) both audio books were always presented equally loud. In Holtze et al. 2021, a 10-minute block could

either be presented in the omnidirectional condition (both audio books were presented

equally loud) or in the beamforming condition (the to-be-attended audio book was louder than the to-be-ignored audio book). The first 10-minute block was always presented in the omnidirectional condition whereas the conditions were alternated for the later four blocks, with one half of the participants starting with the omnidirectonal condition and the other half starting with the beamforming condition The article (see Reference) contains all methodological details

Bj�rn Holtze (December, 2022)

Dataset Information#

Dataset ID	`DS004369`
Title	Blink-Pause-Relation (Competing Speaker Paradigm)
Year	2022
Authors	Bjoern Holtze, Marc Rosenkranz, Martin Bleichner, Stefan Debener
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004369.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: 41
Recordings: 345
Tasks: 1

Channels & sampling rate

Channels: 7 (41), 4 (41)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS004369 class to access this dataset programmatically.

class eegdash.dataset.DS004369(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004369. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 41; recordings: 41; 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/ds004369 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004369

Examples

>>> from eegdash.dataset import DS004369
>>> dataset = DS004369(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