DS005932#

PWIe

Access recordings and metadata through EEGDash.

Citation: Phillip J. Holcomb, Jacklyn Jardel, Katherine J. Midgley, and Karen Emmorey (2025). PWIe. 10.18112/openneuro.ds005932.v1.0.0

Modality: eeg Subjects: 29 Recordings: 151 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.

Dataset Information#

Dataset ID	`DS005932`
Title	PWIe
Year	2025
Authors	Phillip J. Holcomb, Jacklyn Jardel, Katherine J. Midgley, and Karen Emmorey
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005932.v1.0.0
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: 29
Recordings: 151
Tasks: 1

Channels & sampling rate

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

API Reference#

Use the DS005932 class to access this dataset programmatically.

class eegdash.dataset.DS005932(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005932. 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

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/ds005932 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005932

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, 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