EEGdash›OpenNeuro›DS003922

Iss. 3922 · 14 subjects · 164 recordings · CC0

Dataset Brief · Multisensory Correlation Detector

DS003922: meg dataset, 14 subjects#

Multisensory Correlation Detector

Citation: Pesnot Lerousseau, J., Parise, C., Ernst, MO., van Wassenhove, V. (2022). Multisensory Correlation Detector. 10.18112/openneuro.ds003922.v1.0.1

14-participant MEG dataset — Multisensory Correlation Detector.

Data & curation Pesnot Lerousseau, J. · Parise, C. · Ernst, MO. · van Wassenhove, V.
Year 2022 · Distributed via OpenNeuro
Funding ERC-YStG-263584 · ANR-16-CE37-0004-04

MEG · 342 (128), 323 (23) ch1000 HzBIDS 1.6.03 tasks9 sessionsHealthyMultisensoryPerception

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#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003922

dataset = DS003922(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 = DS003922(cache_dir="./data", subject="01")

Advanced query

dataset = DS003922(
    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{ds003922,
  title = {Multisensory Correlation Detector},
  author = {Pesnot Lerousseau, J. and Parise, C. and Ernst, MO. and van Wassenhove, V.},
  doi = {10.18112/openneuro.ds003922.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds003922.v1.0.1},
}

§ 02Study · The README

About This Dataset#

Magnetoencephalography (MEG) dataset recorded during the presentation of audiovisual sequences with a causality judgment task and temporal order judgment task. This MEG dataset was prepared in the Brain Imaging Data Structure (MEG-BIDS, Niso et al. 2018) format using MNE-BIDS (Appelhoff et al. 2019).

Pesnot Lerousseau, J., Parise, C., Ernst, MO., van Wassenhove, V. (2022). Multisensory correlation computations in the human brain identified by a time-resolved encoding model. *Nature Communications*. http://doi.org/10.1038/s41467-022-29687-6

DESCRIPTION

PARTICIPANTS

The dataset contains 13 participants (Ab140232, Jl150443, Mm150194, Al150424, Mp110340, Rt160359, Cb140229, Cc160310, Lb160367, Mb160304, Mk150295, Sl160372, Mp150285).

EXPERIMENT

The experiment consisted of 10 consecutive recording blocks of 8 minutes each, whose order was counterbalanced across participants. Three blocks tested participants on a Causality judgement, and three blocks tested participants with a Temporal order judgement. Importantly, the same audiovisual sequences were used in both tasks in order to maintain a constant flow of feedforward multisensory inputs while manipulating the endogenous task requirements. Each block was composed of 25 repetitions of the 6 possible audiovisual sequences. A total of 75 presentations of each stimulus sequence were thus tested in each task. Four additional recording blocks consisted of participants passively hearing (auditory localizer, 2 blocks) or viewing (visual localizer, 2 blocks) one constitutive modality of the audiovisual sequence. Each localizer block was composed of 25 repetitions of the 6 possible stimuli (auditory or visual part of each stimuli), yielding a total of 50 presentations of each auditory and visual stimuli (2 tasks x 3 blocks x 25 repetitions x 6 sequences + 2 modalities x 25 repetitions x 2 blocks x 6 sequences = 1500 trials in total).

View full README

DESCRIPTION

PARTICIPANTS

The dataset contains 13 participants (Ab140232, Jl150443, Mm150194, Al150424, Mp110340, Rt160359, Cb140229, Cc160310, Lb160367, Mb160304, Mk150295, Sl160372, Mp150285).

EXPERIMENT

The experiment consisted of 10 consecutive recording blocks of 8 minutes each, whose order was counterbalanced across participants. Three blocks tested participants on a Causality judgement, and three blocks tested participants with a Temporal order judgement. Importantly, the same audiovisual sequences were used in both tasks in order to maintain a constant flow of feedforward multisensory inputs while manipulating the endogenous task requirements. Each block was composed of 25 repetitions of the 6 possible audiovisual sequences. A total of 75 presentations of each stimulus sequence were thus tested in each task. Four additional recording blocks consisted of participants passively hearing (auditory localizer, 2 blocks) or viewing (visual localizer, 2 blocks) one constitutive modality of the audiovisual sequence. Each localizer block was composed of 25 repetitions of the 6 possible stimuli (auditory or visual part of each stimuli), yielding a total of 50 presentations of each auditory and visual stimuli (2 tasks x 3 blocks x 25 repetitions x 6 sequences + 2 modalities x 25 repetitions x 2 blocks x 6 sequences = 1500 trials in total).

STIMULI

Six audiovisual sequences were presented (DD, DC, CC, AA, AV, VV).

BLOCKS

Ten blocks were presented (3 Causality, 3 Temporal, 2 Auditory, 2 Visual).

EVENTS

‘Causality/DD’:11
‘Causality/DC’:12
‘Causality/CC’:13
‘Causality/AA’:14
‘Causality/AV’:15
‘Causality/VV’:16
‘Temporal/DD’:21
‘Temporal/DC’:22
‘Temporal/CC’:23
‘Temporal/AA’:24
‘Temporal/AV’:25
‘Temporal/VV’:26
‘Auditory/DD’:41
‘Auditory/DC’:42
‘Auditory/CC’:43
‘Auditory/AA’:44
‘Auditory/AV’:45
‘Auditory/VV’:46
‘Visual/DD’:51
‘Visual/DC’:52
‘Visual/CC’:53
‘Visual/AA’:54
‘Visual/AV’:55
‘Visual/VV’:56

MEG

Brain magnetic fields were recorded in a MSR using a 306 MEG system (Neuromag Elekta LTD, Helsinki). MEG recordings were sampled at 1 kHz and band-pass filtered between 0.03 Hz and 330 Hz.

Four head position coils (HPI) measured the head position of participants before each block; three fiducial markers (nasion and pre-auricular points) were used for digitization and anatomicalMRI (aMRI) immediately following MEG acquisition. Electrooculograms (EOG, horizontal and vertical eye movements) and electrocardiogram (ECG) were simultaneously recorded. Prior to the session, 2 min of empty room recordings was acquired for the computation of the noise covariance matrix.

Bad MEG channels were marked manually.

MRI

The T1 weighted aMRI was recorded using a 3-T Siemens Trio MRI scanner. Parameters of the sequence were: voxel size: 1.0 × 1.0 × 1.1 mm; acquisition time: 466 s; repetition time TR = 2300 ms; and echo time TE = 2.98 ms

BEHAVIOR

File sourcedata/behavioral_data.txt

REFERENCES

Pesnot Lerousseau, J., Parise, C., Ernst, MO., van Wassenhove, V. (2022). Multisensory correlation computations in the human brain identified by a time-resolved encoding model. Nature Communications. http://doi.org/10.1038/s41467-022-29687-6 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. http://doi.org/10.1038/sdata.2018.110

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=14, range 21–88 yr, mean 29.4 yr)

20253085

Female · 9Male · 5

Sex composition

Female
9
Male
5
F : M ratio
1.80 : 1

64% female · n = 14 subjects with reported sex.

HandednessRight · 14

Channel counts (ch)

323342

Sampling frequencies: 1000.0 Hz (n=151 recordings)

Total recording duration: 16 h 29 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 342 (128), 323 (23) ch · MEG · 1000 Hz · 14 subjects, 164 recordings

Live trace viewer — sub-emptyroom · ses-20161129 · task-noise

Showing one representative recording out of 14 subjects and 164 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

§ 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…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS003922(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Lerousseau2021

Canonical—

Importable asDS003922 · Lerousseau2021

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.DS003922(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Multisensory Correlation Detector

Study:: ds003922 (OpenNeuro)
Author (year):: Lerousseau2021
Canonical:: —

Also importable as: DS003922, Lerousseau2021.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 14; recordings: 164; 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/ds003922 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003922 DOI: https://doi.org/10.18112/openneuro.ds003922.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003922
>>> dataset = DS003922(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/ds003922 · pull with datasets.load_dataset("EEGDash/ds003922").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS003922.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 ds003922 to reproduce the tutorial on this dataset.

Citation

Pesnot Lerousseau, J., Parise, C., Ernst, MO., van Wassenhove, V. (2022). Multisensory Correlation Detector. 10.18112/openneuro.ds003922.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.ds003922.v1.0.1.

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+ 1 more — see See Also below →

BIDS

BIDS 1.6.0

Sidecars

coordsystem

Provenance

CC0 · 10.18112/openneuro.ds003922.v1.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS003922`
Title	Multisensory Correlation Detector
Author (year)	`Lerousseau2021`
Canonical	—
Importable as	`DS003922`, `Lerousseau2021`
Year	2022
Authors	Pesnot Lerousseau, J., Parise, C., Ernst, MO., van Wassenhove, V.
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003922.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS003922: meg dataset, 14 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#