ON005279: meg dataset, 30 subjects#
Picture-Word Interference Dataset
Access recordings and metadata through EEGDash.
Citation: Hsi T. Wei, Farhan B. Faisal, Tamara Beck, Claire Shao, Jed A. Meltzer (—). Picture-Word Interference Dataset. 10.82901/nemar.on005279
Modality: meg Subjects: 30 Recordings: 90 License: CC0 Source: nemar
Metadata: Good (80%)
30-participant MEG dataset — Picture-Word Interference Dataset.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON005279
dataset = ON005279(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON005279(cache_dir="./data", subject="01")
Advanced query
dataset = ON005279(
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{on005279,
title = {Picture-Word Interference Dataset},
author = {Hsi T. Wei and Farhan B. Faisal and Tamara Beck and Claire Shao and Jed A. Meltzer},
doi = {10.82901/nemar.on005279},
url = {https://doi.org/10.82901/nemar.on005279},
}
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.
Cohort#
Signal · Electrodes & live trace#
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 |
Picture-Word Interference Dataset |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Hsi T. Wei, Farhan B. Faisal, Tamara Beck, Claire Shao, Jed A. Meltzer |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on005279,
title = {Picture-Word Interference Dataset},
author = {Hsi T. Wei and Farhan B. Faisal and Tamara Beck and Claire Shao and Jed A. Meltzer},
doi = {10.82901/nemar.on005279},
url = {https://doi.org/10.82901/nemar.on005279},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON005279(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Picture-Word Interference Dataset
- Study:
on005279(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON005279.Modality:
meg; Subject type:Unknown. 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
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/on005279 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on005279 DOI: https://doi.org/10.82901/nemar.on005279
Examples
>>> from eegdash.dataset import ON005279 >>> dataset = ON005279(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.pytorch
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 on005279 to reproduce the tutorial on this dataset.
Citation
Hsi T. Wei, Farhan B. Faisal, Tamara Beck, Claire Shao, Jed A. Meltzer (n.d.). Picture-Word Interference Dataset. 10.82901/nemar.on005279
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on005279.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset