DS006720: meg dataset, 24 subjects#
Alpha power indexes working memory load for durations
Citation: Sophie K. Herbst [1], Izem Mangione [1], Charbel-Raphaël Segerie [2], Richard Höchenberger [2], Tadeusz Kononowicz [1, 3, 4], Alexandre Gramfort [2], Virginie van Wassenhove [1], [1] Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin, 91191 Gif/Yvette, France [2] Inria, CEA, Université Paris-Saclay, Palaiseau, France [3] Institute of Psychology, The Polish Academy of Sciences, ul. Jaracza 1, 00-378 Warsaw, Poland [4] Institut NeuroPSI - UMR9197 CNRS Université Paris-Saclay (2019). Alpha power indexes working memory load for durations. 10.18112/openneuro.ds006720.v1.0.0
24-participant MEG dataset — Alpha power indexes working memory load for durations.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS006720
dataset = DS006720(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS006720(cache_dir="./data", subject="01")
Advanced query
dataset = DS006720(
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{ds006720,
title = {Alpha power indexes working memory load for durations},
author = {Sophie K. Herbst [1] and Izem Mangione [1] and Charbel-Raphaël Segerie [2] and Richard Höchenberger [2] and Tadeusz Kononowicz [1, 3, 4] and Alexandre Gramfort [2] and Virginie van Wassenhove [1] and [1] Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin, 91191 Gif/Yvette, France [2] Inria, CEA, Université Paris-Saclay, Palaiseau, France [3] Institute of Psychology, The Polish Academy of Sciences, ul. Jaracza 1, 00-378 Warsaw, Poland [4] Institut NeuroPSI - UMR9197 CNRS Université Paris-Saclay},
doi = {10.18112/openneuro.ds006720.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds006720.v1.0.0},
}
About This Dataset#
The data set contains anonymized raw magnetoencephalography (MEG) recordings of 23 healthy adult participants, performed at Neurospin, Gif sur Yvette, France. Participants performed an n-item delayed temporal reproduction task: They were presented with a sequence of one or three “empty” intervals (see cover figure), delimited by short pure tones. They had to maintain the sequence in memory (retention), and, upon a prompt, reproduce the whole sequence by pressing a button for each tone. Eight task runs were recorded (~ 10 min each). The dataset also contains recordings of the electro-occulogram (EOG, horizontal and vertical eye movements) and -cardiogram (ECG), and the 3D coordinates of the EEG electrodes, four head-position indicator coils, and three fiducial points (nasion, left and right pre-auricular areas). A two-minute-long resting state recording (eyes open) was performed after the task. To improve the spatial resolution of the source reconstruction, individual high-resolution structural Magnetic Resonance Imaging (MRI) recordings were acquired. The data are reusable for researchers with a dedicated interest in the neural dynamics of working memory, but also to a broader community interested in neural dynamics in the healthy adult brain, in relation to auditory stimuli, motor responses, and during periods of rest.
The data were formatted in BIDS and anonymized using the following software:
MNE Python version 1.8.0 MNE-BIDS version 1.6.0 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 MEG recording:
Before undergoing the MEG recording, participants were equipped with external electrodes, positioned to record the electro-occulogram (EOG, horizontal and vertical eye movements) and -cardiogram (ECG). The positions of the EEG electrodes, four head-position indicator coils, and three fiducial points (nasion, left and right pre-auricular areas) were digitized using a 3D digitizer (Polhemus, US/Canada) for subsequent co-registration with the individual's anatomical MRI. The MEG recordings took place in a magnetically shielded chamber, where the participant was seated in an armchair under the MEG helmet. The electromagnetic brain activity was recorded using a whole-head Elekta Neuromag Vector View 306 MEG system (Neuromag Elekta LTD, Helsinki) with 102 triple-sensors elements (two orthogonal planar gradiometers, and one magnetometer per sensor location). Participants were instructed to fixate their gaze on a screen positioned in front of them, at about one meter distance. The chamber was dimly lit. Their head position was measured before each recording run (8 in total) using the head-position indicator coils. MEG recordings were sampled online at 1 kHz, high-pass filtered at 0.03 Hz, and low-pass filtered at 330 Hz. A two-minute-long resting state recording (eyes open) was performed after the task, used to compute the noise covariance matrix for source reconstruction.
Anatomical MRI recordings:
To improve the spatial resolution of the source reconstruction, individual high-resolution structural Magnetic Resonance Imaging (MRI) recordings were used. These were recorded on another day, using a Siemens 3 T Magnetom Prisma Fit MRI scanner. Parameters of the sequence were: slice thickness: 1 mm, repetition time TR = 2300 ms, echo time TE = 2.98 ms, and flip angle = 9 degrees.
Cohort#
Dataset Statistics#
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 30 h
Signal · Electrodes & live trace#
Live trace viewer — sub-840 · task-tiwm · run-04
Showing one representative recording out of
24 subjects and 246 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 |
Alpha power indexes working memory load for durations |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2019 |
Authors |
Sophie K. Herbst [1], Izem Mangione [1], Charbel-Raphaël Segerie [2], Richard Höchenberger [2], Tadeusz Kononowicz [1, 3, 4], Alexandre Gramfort [2], Virginie van Wassenhove [1], [1] Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin, 91191 Gif/Yvette, France [2] Inria, CEA, Université Paris-Saclay, Palaiseau, France [3] Institute of Psychology, The Polish Academy of Sciences, ul. Jaracza 1, 00-378 Warsaw, Poland [4] Institut NeuroPSI - UMR9197 CNRS Université Paris-Saclay |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds006720,
title = {Alpha power indexes working memory load for durations},
author = {Sophie K. Herbst [1] and Izem Mangione [1] and Charbel-Raphaël Segerie [2] and Richard Höchenberger [2] and Tadeusz Kononowicz [1, 3, 4] and Alexandre Gramfort [2] and Virginie van Wassenhove [1] and [1] Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin, 91191 Gif/Yvette, France [2] Inria, CEA, Université Paris-Saclay, Palaiseau, France [3] Institute of Psychology, The Polish Academy of Sciences, ul. Jaracza 1, 00-378 Warsaw, Poland [4] Institut NeuroPSI - UMR9197 CNRS Université Paris-Saclay},
doi = {10.18112/openneuro.ds006720.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds006720.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS006720 · Herbst2025eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS006720(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Alpha power indexes working memory load for durations
- Study:
ds006720(OpenNeuro)- Author (year):
Herbst2025- Canonical:
—
Also importable as:
DS006720,Herbst2025.Modality:
meg; Experiment type:Memory; Subject type:Healthy. Subjects: 24; recordings: 246; tasks: 3.- 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/ds006720 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006720 DOI: https://doi.org/10.18112/openneuro.ds006720.v1.0.0
Examples
>>> from eegdash.dataset import DS006720 >>> dataset = DS006720(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/ds006720").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 ds006720 to reproduce the tutorial on this dataset.
Citation
Sophie K. Herbst [1], Izem Mangione [1], Charbel-Raphaël Segerie [2], Richard Höchenberger [2], Tadeusz Kononowicz [1, 3, 4], … (2019). Alpha power indexes working memory load for durations. 10.18112/openneuro.ds006720.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.ds006720.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset