DS005565#

Neural associations between fingerspelling, print, and signs: An ERP priming study with deaf readers

Access recordings and metadata through EEGDash.

Citation: Brittany Lee, Sofia E. Ortega, Priscilla M. Martinez, Katherine J. Midgley, Phillip J. Holcomb, Karen Emmorey (2024). Neural associations between fingerspelling, print, and signs: An ERP priming study with deaf readers. 10.18112/openneuro.ds005565.v1.0.3

Modality: eeg Subjects: 24 Recordings: 131 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005565

dataset = DS005565(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 = DS005565(cache_dir="./data", subject="01")

Advanced query

dataset = DS005565(
    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{ds005565,
  title = {Neural associations between fingerspelling, print, and signs: An ERP priming study with deaf readers},
  author = {Brittany Lee and Sofia E. Ortega and Priscilla M. Martinez and Katherine J. Midgley and Phillip J. Holcomb and Karen Emmorey},
  doi = {10.18112/openneuro.ds005565.v1.0.3},
  url = {https://doi.org/10.18112/openneuro.ds005565.v1.0.3},
}

About This Dataset#

Data collection took place at the NeuroCognition Laboratory (NCL) in San Diego, California under the supervision of Dr. Phillip Holcomb. This project followed the San Diego State University’s IRB guidelines.

Participants sat in a comfortable chair in a darkened sound attenuated room throughout the experiment. They were given a gamepad for button pressing. They were instructed to watch the LCD video monitor that was at a viewing distance of 150cm.

Participants were presented with 300 prime-target pairs. All targets were four-letter English words. Of the 300 critical trials, 100 had English word primes, 100 had ASL sign primes, and 100 had fingerspelled word primes. Half of the primes in each condition were related to the targets. Related English word primes were identity primes to the English word, related fingerspelled word primes were also identity primes, and related ASL primes were ASL translations of the English word targets. The other half of the primes were unrelated to the targets.

Participants were instructed to focus on the purple fixation cross that appeared on the screen for 800ms. This fixation cross then turned white for 500ms. Then, one of three prime conditions was presented: an English word, an ASL sign, or a fingerspelled word. English prime words were presented for 300ms. Signed (M = 565ms) and fingerspelled (M = 1173ms) video primes had variable durations. All target stimuli were 4-letter English words presented for 500ms. Related primes were either identity or translations.

Press any of the 4 buttons on the right of the gamepad whenever you see an animal. It doesn’t matter if the animal is presented as a sign, a word, or fingerspelled. Press for ANY animal. You can blink whenever you see purple. A purple + means you have time for a quick blink. A purple (–) means you can blink as much as you want.

Dataset Information#

Dataset ID	`DS005565`
Title	Neural associations between fingerspelling, print, and signs: An ERP priming study with deaf readers
Year	2024
Authors	Brittany Lee, Sofia E. Ortega, Priscilla M. Martinez, Katherine J. Midgley, Phillip J. Holcomb, Karen Emmorey
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005565.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: 24
Recordings: 131
Tasks: 1

Channels & sampling rate

Channels: 32
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS005565 class to access this dataset programmatically.

class eegdash.dataset.DS005565(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005565. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 24; recordings: 24; 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/ds005565 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005565

Examples

>>> from eegdash.dataset import DS005565
>>> dataset = DS005565(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