DS005279#

Picture-Word Interference Dataset

Access recordings and metadata through EEGDash.

Citation: Hsi T. Wei, Farhan B. Faisal, Tamara Beck, Claire Shao, Jed A. Meltzer (2024). Picture-Word Interference Dataset. 10.18112/openneuro.ds005279.v1.0.3

Modality: meg Subjects: 30 Recordings: 90 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005279

dataset = DS005279(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 = DS005279(cache_dir="./data", subject="01")

Advanced query

dataset = DS005279(
    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{ds005279,
  title = {Picture-Word Interference Dataset},
  author = {Hsi T. Wei and Farhan B. Faisal and Tamara Beck and Claire Shao and Jed A. Meltzer},
  doi = {10.18112/openneuro.ds005279.v1.0.3},
  url = {https://doi.org/10.18112/openneuro.ds005279.v1.0.3},
}

About This Dataset#

This study was conducted at the Rotman Research Institute at Baycrest Hospital in Toronto, Canada. This dataset contains 30 healthy young adults’ MEG (CTF), sMRI, and behavioural data on a picture-word interference (PWI) task. Subjects were shown images of objects one by one and were instructed to retrieve the name of the pictures covertly and judge whether the name ends in a target sound given at the beginning of each task block, by pressing the yes or no buttons with their right hand. Whenever they see an image, they will often also hear a distractor word played through their earphone. The picture and word could be phonologically related, semantically related, or unrelated. There were 3 runs of the PWI task for each participant. Each run contained 120 trials, containing an equal number of trials for each picture-word condition. Behaviourally, the reaction time and accuracy of their button-pressing response were recorded. Meanwhile, the MEG data was epoched to the picture onset and response onset for event-related analyses. Each subject obtained their own structural MRI for MEG source localization. Corresponding analysis code can be found under the code folder, with the “analysis walkthrough” documenting more detailed explanation of the analysis.

Dataset Information#

Dataset ID	`DS005279`
Title	Picture-Word Interference Dataset
Year	2024
Authors	Hsi T. Wei, Farhan B. Faisal, Tamara Beck, Claire Shao, Jed A. Meltzer
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005279.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: 30
Recordings: 90
Tasks: —

Channels & sampling rate

Channels: Varies
Sampling rate (Hz): 1200.0
Duration (hours): 0.0

API Reference#

Use the DS005279 class to access this dataset programmatically.

class eegdash.dataset.DS005279(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005279. Modality: meg; Experiment type: Other; Subject type: Healthy. Subjects: 30; recordings: 90; tasks: 0.

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/ds005279 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005279

Examples

>>> from eegdash.dataset import DS005279
>>> dataset = DS005279(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