DS008017: meg dataset, 15 subjects#
Learning regularities in noise (ASRT MEG)
Access recordings and metadata through EEGDash.
Citation: Coumarane Tirou, Oussame Abdoun, Teodóra Vékony, Laure Tosatto, Andrea Brovelli, Marine Vernet, Dezső Németh, Romain Quentin (2026). Learning regularities in noise (ASRT MEG). 10.18112/openneuro.ds008017.v1.0.0
Modality: meg Subjects: 15 Recordings: 143 License: CC0 Source: openneuro
Metadata: Complete (100%)
15-participant MEG dataset — Learning regularities in noise (ASRT MEG).
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS008017
dataset = DS008017(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS008017(cache_dir="./data", subject="01")
Advanced query
dataset = DS008017(
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{ds008017,
title = {Learning regularities in noise (ASRT MEG)},
author = {Coumarane Tirou and Oussame Abdoun and Teodóra Vékony and Laure Tosatto and Andrea Brovelli and Marine Vernet and Dezső Németh and Romain Quentin},
doi = {10.18112/openneuro.ds008017.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds008017.v1.0.0},
}
About This Dataset#
This dataset contains magnetoencephalography (MEG) recordings collected while participants performed the cued Alternating Serial Reaction Time task, a visuo-motor task during which arrows are displayed and they must respond with response keys.
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
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
Summary
Cohort#
Dataset Statistics#
Channel counts: 277 ch (n=143 recordings)
Sampling frequencies: 2034.5100996195154 Hz (n=8 recordings)
Total recording duration: 58 min
Signal · Electrodes & live trace#
Live trace viewer — sub-14 · task-asrt · run-03
Showing one representative recording out of
15 subjects and 143 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _meg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?meg=<url>) to inspect it.
Electrode layout — MEG · 248 sensors — 248 channels
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 |
Learning regularities in noise (ASRT MEG) |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2026 |
Authors |
Coumarane Tirou, Oussame Abdoun, Teodóra Vékony, Laure Tosatto, Andrea Brovelli, Marine Vernet, Dezső Németh, Romain Quentin |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds008017,
title = {Learning regularities in noise (ASRT MEG)},
author = {Coumarane Tirou and Oussame Abdoun and Teodóra Vékony and Laure Tosatto and Andrea Brovelli and Marine Vernet and Dezső Németh and Romain Quentin},
doi = {10.18112/openneuro.ds008017.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds008017.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.DS008017(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Learning regularities in noise (ASRT MEG)
- Study:
ds008017(OpenNeuro)- Author (year):
—
- Canonical:
—
Also importable as:
DS008017.Modality:
meg; Subject type:Unknown. Subjects: 15; recordings: 143; 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
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/ds008017 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds008017 DOI: https://doi.org/10.18112/openneuro.ds008017.v1.0.0
Examples
>>> from eegdash.dataset import DS008017 >>> dataset = DS008017(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 ds008017 to reproduce the tutorial on this dataset.
Citation
Coumarane Tirou, Oussame Abdoun, Teodóra Vékony, Laure Tosatto, Andrea Brovelli, … (2026). Learning regularities in noise (ASRT MEG). 10.18112/openneuro.ds008017.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.ds008017.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset