DS006468#

MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps And Noisy Sentences.

Access recordings and metadata through EEGDash.

Citation: Till Habersetzer, Bernd T. Meyer (2025). MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps And Noisy Sentences.. 10.18112/openneuro.ds006468.v1.1.2

Modality: meg Subjects: 24 Recordings: 1071 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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#

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

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

Description

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].

Dataset Information#

Dataset ID	`DS006468`
Title	MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps And Noisy Sentences.
Year	2025
Authors	Till Habersetzer, Bernd T. Meyer
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006468.v1.1.2
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: 24
Recordings: 1071
Tasks: 8

Channels & sampling rate

Channels: 306 (160), 341 (153), 347 (7), 32 (5), 372 (5)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS006468 class to access this dataset programmatically.

class eegdash.dataset.DS006468(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006468. 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

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/ds006468 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006468

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, 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