DS005565: eeg dataset, 24 subjects#
Neural associations between fingerspelling, print, and signs: An ERP priming study with deaf readers
Citation: Brittany Lee, Sofia E. Ortega, Priscilla M. Martinez, Katherine J. Midgley, Phillip J. Holcomb, Karen Emmorey (—). Neural associations between fingerspelling, print, and signs: An ERP priming study with deaf readers. 10.18112/openneuro.ds005565.v1.0.3
24-participant EEG dataset — Neural associations between fingerspelling, print, and signs: An ERP priming study with deaf readers.
Quickstart#
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)
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'])
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.
Cohort#
Dataset Statistics#
Age distribution (n=24, range 20–53 yr, mean 33.4 yr · sex per subject not reported)
Sex composition
Channel counts: 32 ch (n=24 recordings)
Sampling frequencies: 500.0 Hz (n=24 recordings)
Total recording duration: 11 h 26 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-SemanticCategorization
Showing one representative recording out of
24 subjects and 24 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
Neural associations between fingerspelling, print, and signs: An ERP priming study with deaf readers |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Brittany Lee, Sofia E. Ortega, Priscilla M. Martinez, Katherine J. Midgley, Phillip J. Holcomb, Karen Emmorey |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005565 · Lee2024_StudyWITHeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005565(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Neural associations between fingerspelling, print, and signs: An ERP priming study with deaf readers
- Study:
ds005565(OpenNeuro)- Author (year):
Lee2024_StudyWITH- Canonical:
—
Also importable as:
DS005565,Lee2024_StudyWITH.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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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 DOI: https://doi.org/10.18112/openneuro.ds005565.v1.0.3 NEMAR citation count: 1
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: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds005565").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds005565 to reproduce the tutorial on this dataset.
Citation
Brittany Lee, Sofia E. Ortega, Priscilla M. Martinez, Katherine J. Midgley, Phillip J. Holcomb, … (n.d.). Neural associations between fingerspelling, print, and signs: An ERP priming study with deaf readers. 10.18112/openneuro.ds005565.v1.0.3
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds005565.v1.0.3.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset