DS005932: eeg dataset, 29 subjects#
PWIe
Citation: Phillip J. Holcomb, Jacklyn Jardel, Katherine J. Midgley, and Karen Emmorey (20). PWIe. 10.18112/openneuro.ds005932.v1.0.0
29-participant EEG dataset — PWIe.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005932
dataset = DS005932(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005932(cache_dir="./data", subject="01")
Advanced query
dataset = DS005932(
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{ds005932,
title = {PWIe},
author = {Phillip J. Holcomb and Jacklyn Jardel and Katherine J. Midgley and and Karen Emmorey},
doi = {10.18112/openneuro.ds005932.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005932.v1.0.0},
}
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 and wore 32 head and face electrodes (left mastoid reference). They were given a gamepad for button pressing and wore a lightweight headset to record their verbal responses. They were instructed to watch the LCD video monitor that was at a viewing distance of 150cm. All stimuli were less than 2° of horizontal and vertical visual angle.
Participants were presented with 100 unique simple black on white to-be-named line drawings, with 50 pictures in the Semantic category and 50 in the Identity category. Each picture was presented twice, once preceded by an unrelated English distractor word and once by a related English distractor word (2000 ms duration). Prime “distractor” words were presented before the picture for 200 ms and were either semantically related, were the same name as the picture, or were unrelated to the picture. Participants were told to name each picture as quickly as possible in English. Their voice response was digitized online. The experiment was self-paced and participants pressed a button after each trial when ready to go on. EEG was sampled continuously at 500 Hz with a bandpass of DC to 200 Hz. Event markers were stored with the EEG data for later ERP averaging. The raw EEG data were imported into EEGLab and saved as .set files. A key to the event code structure is contained in the PWIe bdf files for each subject.
Cohort#
Dataset Statistics#
Age distribution (n=29, range 19–35 yr, mean 24.4 yr · sex per subject not reported)
Sex composition
Channel counts: 32 ch (n=29 recordings)
Sampling frequencies: 500.0 Hz (n=29 recordings)
Total recording duration: 9 h 56 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-PictureWordInterference
Showing one representative recording out of
29 subjects and 29 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 |
PWIe |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Phillip J. Holcomb, Jacklyn Jardel, Katherine J. Midgley, and Karen Emmorey |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds005932,
title = {PWIe},
author = {Phillip J. Holcomb and Jacklyn Jardel and Katherine J. Midgley and and Karen Emmorey},
doi = {10.18112/openneuro.ds005932.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005932.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005932 · Holcomb2025eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005932(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
PWIe
- Study:
ds005932(OpenNeuro)- Author (year):
Holcomb2025- Canonical:
—
Also importable as:
DS005932,Holcomb2025.Modality:
eeg; Experiment type:Other; Subject type:Healthy. Subjects: 29; recordings: 29; 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/ds005932 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005932 DOI: https://doi.org/10.18112/openneuro.ds005932.v1.0.0
Examples
>>> from eegdash.dataset import DS005932 >>> dataset = DS005932(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/ds005932").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 ds005932 to reproduce the tutorial on this dataset.
Citation
Phillip J. Holcomb, Jacklyn Jardel, Katherine J. Midgley, and Karen Emmorey (20). PWIe. 10.18112/openneuro.ds005932.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds005932.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset