DS006720#

Alpha power indexes working memory load for durations

Access recordings and metadata through EEGDash.

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 (2025). Alpha power indexes working memory load for durations. 10.18112/openneuro.ds006720.v1.0.0

Modality: meg Subjects: 23 Recordings: 1010 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.

Dataset Information#

Dataset ID	`DS006720`
Title	Alpha power indexes working memory load for durations
Year	2025
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	doi:10.18112/openneuro.ds006720.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: 23
Recordings: 1010
Tasks: 3

Channels & sampling rate

Channels: 306 (222), 328 (209), 321 (11), 340 (2), 61, 390
Sampling rate (Hz): 1000.0 (444), 2000.0 (2)
Duration (hours): 0.0

API Reference#

Use the DS006720 class to access this dataset programmatically.

class eegdash.dataset.DS006720(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006720. 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

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/ds006720 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006720

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, 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