EEGdash›OpenNeuro›DS002550
Iss. 2550 · 22 subjects · 377 recordings · CC0
Dataset Brief · Differential brain mechanisms of selection and maintenance of…

DS002550: meg dataset, 22 subjects#

Differential brain mechanisms of selection and maintenance of information during working memory (MEG data)

Citation: Romain Quentin, Jean-Remi King, Etienne Sallard, Nathan Fishman, Ryan Thompson, Ethan Buch, Leonardo Cohen (20). Differential brain mechanisms of selection and maintenance of information during working memory (MEG data). 10.18112/openneuro.ds002550.v1.0.1

22-participant MEG dataset — Differential brain mechanisms of selection and maintenance of information during working memory (MEG data).

Data & curation Romain Quentin · Jean-Remi King · Etienne Sallard · Nathan Fishman · Ryan Thompson · Ethan Buch · …
Year 20 · Distributed via OpenNeuro
Funding NINDS/HCPS · FYSSEN foundation · + 1 more
MEG · 308 (367), 307 (8), 304 (2) ch1200 Hz · mixedBIDS 1.1.12 tasks2 sessionsHealthyVisualMemory
Layer 01Study
What was asked
Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.
Layer 02Signal · BIDS
What was recorded
Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.
Layer 03Training · ML
What you can train on
Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.
§ 01Access · Get started

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS002550

dataset = DS002550(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS002550(cache_dir="./data", subject="01")

Advanced query

dataset = DS002550(
    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{ds002550,
  title = {Differential brain mechanisms of selection and maintenance of information during working memory (MEG data)},
  author = {Romain Quentin and Jean-Remi King and Etienne Sallard and Nathan Fishman and Ryan Thompson and Ethan Buch and Leonardo Cohen},
  doi = {10.18112/openneuro.ds002550.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds002550.v1.0.1},
}
§ 02Study · The README

About This Dataset#

OpenNeuro curator note: This dataset was previously accessible at ds001750. The dataset was reuploaded due to privacy considerations.

Note: One participant didn’t sign a sharing data agreement so data of 22 participants are available here (vs. 23 in the manuscript). Results and conclusion are not different with only 22 participants.

Participant folder are organized as: - ##### ‘ses-mri/anat’:

contains T1 MRI of the participant - ##### ses-01:

contains MEG data in BIDS format, behavioral data and HPI position in surface RAS MRI coordinates for session 1 - ##### ses-02:

contains MEG data in BIDS format, behavioral data and HPI position in surface RAS MRI coordinates for session 2

View full README

Participant folder are organized as: - ##### ‘ses-mri/anat’:

contains T1 MRI of the participant - ##### ses-01:

contains MEG data in BIDS format, behavioral data and HPI position in surface RAS MRI coordinates for session 1 - ##### ses-02:

contains MEG data in BIDS format, behavioral data and HPI position in surface RAS MRI coordinates for session 2

Description of non-MEG files:

  • ##### behavioral task scripts:

Matlab (psychtoolbox 3) script for Working Memory (WorkMem) and one-back control task (LocaCue) - ##### hpi_mri_surf.txt:

Contains the X, Y Z coordinates of the nasion, left and right HPI (head position indicator) in surface MRI coordinates. Names of the electrode are NEC (nasion), LEC (left) and REC (right). Others coordinates are for co-registration during the session (not useful here). These HPI coordinates have been acquired from the neuronavigation system brainsight (https://www.rogue-research.com/tms/brainsight-tms/) - ##### WorkMem+subNumber+date.csv:

Contains behavioral results:

  • NbTrial: trial number

  • FixNbTrial: trial number with good eye fixation

  • isFixed: whether the participant fixed the central dot during the trial (1:correct fixation, 0:broke fixation)

  • GaborLeft: left gabor (25 possible, 5 spatial frequency* 5 orientations)

  • GaborRight: right gabor (25 possible, 5 spatial frequency* 5 orientations)

  • Cue: cue (4 possible, 1: left dotted, 2: left solid, 3: right dotted, 4: left solid)

  • Change: whether the cued stimulus attribute is different from the corresponding probe attribute (1: different, 0: same)

  • sfLeft: spatial frequency of the left gabor (5 possible)

  • orientLeft: line orientation of the left gabor (5 possible)

  • phaseLeft: phase of the left gabor (5 possible)

  • sfRight: spatial frequency of the right gabor (5 possible)

  • orientRight: line orientation of the right gabor (5 possible)

  • phaseRight: phase of the right gabor (5 possible)

  • randomSF: probe spatial frequency if change=1

  • randomOrient: line orientation if change=1

  • phaseResp: phase of the probe

  • Response: response of the participant (1: different, 0: similar)

  • isCorrect: correctness of the response (1: correct, 0: uncorrect)

  • reactionTime: reaction time from probe onset to participant response

  • TrialTime: total trial duration

  • runningTime: running time

  • fixcrossTime: duration of the fixation dot presentation before stimulus onset (should be between 0.350 and 0.450 s)

  • gaborTime: duration of stimulus presentation (should be 0.1 s)

  • precueTime: duration between stimulus offset and cue onset (should be between 0.75 and 0.85 s)

  • cueTime: duration of the cue presentation (should be 0.1 s)

  • postcueTime: duration between cue offset and probe onset (should be between 1.45 and 1.55 s)

  • feedbackTime: duration of the feedback (green or red dot, should be 0.1 s)

  • triggGabor: trigger sent to MEG acquisition at the stimulus onset

  • triggCue: trigger sent to MEG acquisition at the cue onset

  • triggProbe: trigger sent to MEG acquisition at the probe onset

  • ##### locacue+subNumber+date.csv:

  • NbTrial: trial number

  • FixNbTrial: trial number with good eye fixation

  • isFixed: whether the participant fixed the central dot during the trial (1:correct fixation, 0:broke fixation)

  • same: whether 2 consecutive lines are similar (1: similar, 0: different)

  • Cue: cue (4 possible, 1: left dotted, 2: left solid, 3: right dotted, 4: left solid)

  • Side: side of the cue (1: right, 0: left)

  • Press: whether the participant press the button (1: press, 0: no press)

  • isCorrect: correctness of the response (1: correct, 0: uncorrect)

  • ReactionTime: reaction time when a button is pressed

  • TrialTime: total trial duration

  • runningTime: running time

  • fixcrossTime: duration of the fixation dot

  • cueTime: duration of the cue presentation (should be 0.1 s)

  • postcueTime: duration between the cue offset and the beginning of the next trial (should be 1.2 s)

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Sex composition

22
subjects
Female
16
Male
6
F : M ratio
2.67 : 1
73% female · n = 22 subjects with reported sex.

Channel counts (ch)

304307308

Sampling frequencies (Hz)

120012000

Total recording duration: 30 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 308 (367), 307 (8), 304 (2) ch · MEG · 1200 Hz · mixed · 22 subjects, 377 recordings

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

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.

NEMAR pipeline statistics — DS002550
HED event descriptors word cloud HED event descriptors word cloud — DS002550
§ 05Manifest · BIDS tree

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.

Recordings—
Files—
Subjects—
Modalities—
Click to load file structure…
Full dataset metadata table

Dataset ID

DS002550

Title

Differential brain mechanisms of selection and maintenance of information during working memory (MEG data)

Author (year)

Quentin2020

Canonical

—

Importable as

DS002550, Quentin2020

Year

20

Authors

Romain Quentin, Jean-Remi King, Etienne Sallard, Nathan Fishman, Ryan Thompson, Ethan Buch, Leonardo Cohen

License

CC0

Citation / DOI

10.18112/openneuro.ds002550.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds002550,
  title = {Differential brain mechanisms of selection and maintenance of information during working memory (MEG data)},
  author = {Romain Quentin and Jean-Remi King and Etienne Sallard and Nathan Fishman and Ryan Thompson and Ethan Buch and Leonardo Cohen},
  doi = {10.18112/openneuro.ds002550.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds002550.v1.0.1},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS002550(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Quentin2020
Canonical—
Importable asDS002550 · Quentin2020
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS002550(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Differential brain mechanisms of selection and maintenance of information during working memory (MEG data)

Study:

ds002550 (OpenNeuro)

Author (year):

Quentin2020

Canonical:

—

Also importable as: DS002550, Quentin2020.

Modality: meg; Experiment type: Memory; Subject type: Healthy. Subjects: 22; recordings: 377; 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

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/ds002550 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002550 DOI: https://doi.org/10.18112/openneuro.ds002550.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS002550
>>> dataset = DS002550(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.

Access modesMNE → braindecode → PyTorch → ML
.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne
BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode
DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr
Hugging FacePre-bundled mirror at EEGDash/ds002550 · pull with datasets.load_dataset("EEGDash/ds002550").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS002550.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons
Examples using EEGDashcurated · start here
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 ds002550 to reproduce the tutorial on this dataset.

Citation

Romain Quentin, Jean-Remi King, Etienne Sallard, Nathan Fishman, Ryan Thompson, … (20). Differential brain mechanisms of selection and maintenance of information during working memory (MEG data). 10.18112/openneuro.ds002550.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds002550.v1.0.1.

BIDS
BIDS 1.1.1
Sidecars
not yet probed
Machine-readable
schema.org/Dataset · Croissant

See Also#