DS003922#

Multisensory Correlation Detector

Access recordings and metadata through EEGDash.

Citation: Pesnot Lerousseau, J., Parise, C., Ernst, MO., van Wassenhove, V. (2021). Multisensory Correlation Detector. 10.18112/openneuro.ds003922.v1.0.1

Modality: meg Subjects: 13 Recordings: 674 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

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)

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'])

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},
}

About This Dataset#

DESCRIPTION

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).

PUBLISHED IN

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

View full README

DESCRIPTION

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).

PUBLISHED IN

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

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

Dataset Information#

Dataset ID

DS003922

Title

Multisensory Correlation Detector

Year

2021

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
Copy-paste 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},
}

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: 13

  • Recordings: 674

  • Tasks: 3

Channels & sampling rate

  • Channels: 306 (151), 342 (128), 323 (23)

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Healthy

  • Modality: Multisensory

  • Type: Perception

Files & format

  • Size on disk: 75.7 GB

  • File count: 674

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds003922.v1.0.1

Provenance

API Reference#

Use the DS003922 class to access this dataset programmatically.

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

Bases: EEGDashDataset

OpenNeuro dataset ds003922. 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

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, 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

See Also#