DS006468: meg dataset, 24 subjects#
MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps And Noisy Sentences.
Citation: Till Habersetzer, Bernd T. Meyer (2019). MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps And Noisy Sentences.. 10.18112/openneuro.ds006468.v1.1.2
24-participant MEG dataset — MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps And Noisy Sentences..
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS006468
dataset = DS006468(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS006468(cache_dir="./data", subject="01")
Advanced query
dataset = DS006468(
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{ds006468,
title = {MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps And Noisy Sentences.},
author = {Till Habersetzer and Bernd T. Meyer},
doi = {10.18112/openneuro.ds006468.v1.1.2},
url = {https://doi.org/10.18112/openneuro.ds006468.v1.1.2},
}
About This Dataset#
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
The MEG-SCANS (Stories, Chirps, And Noisy Sentences) dataset provides raw and MaxFiltered magnetoencephalography (MEG) recordings from 24 German-speaking participants, collected over three months. Each participant engaged in an auditory experiment, listening to approximately one hour of stimuli, including two audiobooks (approx. 20 minutes each), 120 sentences from the Oldenburger Matrix Sentence Test (OLSA) presented at varying speech intelligibility levels (20% to 95%) for Speech Reception Threshold (SRT) assessment, and short up-chirps used for MEG signal quality assessment. For each participant, the dataset comprises raw MEG data, corresponding MaxFiltered data, two empty-room MEG recordings (pre- and post-session), a structural MRI scan of the head, behavioral audiogram and SRT results from hearing screenings, and the corresponding audio stimulus material (audiobooks, envelopes, and chirp stimuli). Auxiliary channels recorded include the left audio channel (MISC001), right audio channel (MISC002), and the instructor’s microphone (MISC007), all sampled at 1000 Hz. Organized according to the Brain Imaging Data Structure (BIDS), this dataset offers a robust benchmark for large-scale encoding/decoding analyses of temporally-resolved brain responses to speech. Note that sub-01 served as a pilot so that its data resembles a slightly different experimental design, specifically lacking chirp stimuli and featuring different audiobooks; this variation is accounted for in the provided analysis pipelines. Comprehensive Matlab and Python code are included alongside the entire analysis pipeline [https://doi.org/10.5281/zenodo.17397581] to replicate key data validations, ensuring transparency and reproducibility. The dataset is described in an accompanying data descriptor paper [https://doi.org/10.1038/s41597-025-06397-4].
References
Cohort#
Dataset Statistics#
Age distribution by gender (n=24, range 20–33 yr, mean 25.1 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 1000.0 Hz (n=165 recordings)
Total recording duration: 21 h 44 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-olsa
Showing one representative recording out of
24 subjects and 189 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 · 306 sensors — 306 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 |
MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps And Noisy Sentences. |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2019 |
Authors |
Till Habersetzer, Bernd T. Meyer |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds006468,
title = {MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps And Noisy Sentences.},
author = {Till Habersetzer and Bernd T. Meyer},
doi = {10.18112/openneuro.ds006468.v1.1.2},
url = {https://doi.org/10.18112/openneuro.ds006468.v1.1.2},
}
API Reference#
eegdash.datasetEEGDashDatasetDS006468 · Habersetzer2025eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS006468(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps And Noisy Sentences.
- Study:
ds006468(OpenNeuro)- Author (year):
Habersetzer2025- Canonical:
—
Also importable as:
DS006468,Habersetzer2025.Modality:
meg; Experiment type:Perception; Subject type:Healthy. Subjects: 24; recordings: 189; tasks: 4.- 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/ds006468 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006468 DOI: https://doi.org/10.18112/openneuro.ds006468.v1.1.2
Examples
>>> from eegdash.dataset import DS006468 >>> dataset = DS006468(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.pytorchdatasets.load_dataset("EEGDash/ds006468").huggingface
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 ds006468 to reproduce the tutorial on this dataset.
Citation
Till Habersetzer, Bernd T. Meyer (2019). MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps And Noisy Sentences.. 10.18112/openneuro.ds006468.v1.1.2
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds006468.v1.1.2.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset