DS004276#

Auditory single word recognition in MEG

Access recordings and metadata through EEGDash.

Citation: Phoebe Gaston, Christian Brodbeck, Colin Phillips, Ellen Lau (2022). Auditory single word recognition in MEG. 10.18112/openneuro.ds004276.v1.0.0

Modality: meg Subjects: 18 Recordings: 135 License: CC0 Source: openneuro Citations: 2.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004276

dataset = DS004276(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 = DS004276(cache_dir="./data", subject="01")

Advanced query

dataset = DS004276(
    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{ds004276,
  title = {Auditory single word recognition in MEG},
  author = {Phoebe Gaston and Christian Brodbeck and Colin Phillips and Ellen Lau},
  doi = {10.18112/openneuro.ds004276.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004276.v1.0.0},
}

About This Dataset#

Auditory single word recognition in MEG

This dataset is described in Gaston et al. (2022).

Stimuli and TextGrids are available from the Massive Auditory Lexical Decision database (Tucker et al., 2019).

Converted to BIDS using MNE-BIDS (Appelhoff et al., 2019; Niso et al., 2018).

References

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

Gaston, P., Brodbeck, C., Phillips, C., & Lau, E. (2022). Auditory word comprehension is less incremental in isolated words. Neurobiology of Language

Niso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110. https://doi.org/10.1038/sdata.2018.110

Tucker, B. V., Brenner, D., Danielson, D. K., Kelley, M. C., Nenadić, F., & Sims, M. (2019). The Massive Auditory Lexical Decision (MALD) database. Behavior Research Methods, 51(3), 1187–1204. https://doi.org/10.3758/s13428-018-1056-1

Dataset Information#

Dataset ID	`DS004276`
Title	Auditory single word recognition in MEG
Year	2022
Authors	Phoebe Gaston, Christian Brodbeck, Colin Phillips, Ellen Lau
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004276.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: 18
Recordings: 135
Tasks: 2

Channels & sampling rate

Channels: 193 (19), 157 (19)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS004276 class to access this dataset programmatically.

class eegdash.dataset.DS004276(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004276. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 19; recordings: 19; tasks: 2.

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/ds004276 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004276

Examples

>>> from eegdash.dataset import DS004276
>>> dataset = DS004276(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