DS003392#

NeuroSpin hMT+ Localizer DATA (MEG & aMRI)

Access recordings and metadata through EEGDash.

Citation: Nicolas Zilber, Philippe Ciuciu, Alexandre Gramfort, Leila Azizi, Virginie van Wassenhove (2020). NeuroSpin hMT+ Localizer DATA (MEG & aMRI). 10.18112/openneuro.ds003392.v1.0.4

Modality: meg Subjects: 11 Recordings: 159 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003392

dataset = DS003392(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 = DS003392(cache_dir="./data", subject="01")

Advanced query

dataset = DS003392(
    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{ds003392,
  title = {NeuroSpin hMT+ Localizer DATA (MEG & aMRI)},
  author = {Nicolas Zilber and Philippe Ciuciu and Alexandre Gramfort and Leila Azizi and Virginie van Wassenhove},
  doi = {10.18112/openneuro.ds003392.v1.0.4},
  url = {https://doi.org/10.18112/openneuro.ds003392.v1.0.4},
}

About This Dataset#

Dataset description: Magnetoencephalography (MEG) dataset recorded during a hMT+ (human visual motion area) localizer task

Published in: Zilber, N., Ciuciu, P., Gramfort, A., Azizi, L., & Van Wassenhove, V. (2014). Supramodal processing optimizes visual perceptual learning and plasticity. Neuroimage, 93, 32-46.

Data curation: Sophie Herbst, Alexandre Gramfort

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).

The dataset contains 10 of the 12 participants from the vision-only training group.

View full README

Dataset description: Magnetoencephalography (MEG) dataset recorded during a hMT+ (human visual motion area) localizer task

Published in: Zilber, N., Ciuciu, P., Gramfort, A., Azizi, L., & Van Wassenhove, V. (2014). Supramodal processing optimizes visual perceptual learning and plasticity. Neuroimage, 93, 32-46.

Data curation: Sophie Herbst, Alexandre Gramfort

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).

The dataset contains 10 of the 12 participants from the vision-only training group.

Two participants were removed, one due to problems with the trigger channel, and one due to different settings in the acquisition preventing us from processing the dataset without prior adjustment.

EXPERIMENT

Participants were presented with a cloud of moving dots, always starting with incoherent movement (up or down result in equal display, due to the incoherence). After 500 ms, the movement became coherent in 50% of the trials (95% coherence, up or down) and remained incoherent in the other 50%, lasting for 1000 ms. Participants were instructed to passively view the stimuli for a total of 120 trials.

Events:

1: coherent / down 2: coherent / up 3: incoherent / down 4: incoherent / up

MEG

Brain magnetic fields were recorded in a MSR using a 306 MEG system (Neuromag Elekta LTD, Helsinki). MEG recordings were sampled at 2 kHz and band-pass filtered between 0.03 and 600 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, 5 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

References

Zilber, N., Ciuciu, P., Gramfort, A., Azizi, L., & Van Wassenhove, V. (2014). Supramodal processing optimizes visual perceptual learning and plasticity. Neuroimage, 93, 32-46.

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	`DS003392`
Title	NeuroSpin hMT+ Localizer DATA (MEG & aMRI)
Year	2020
Authors	Nicolas Zilber, Philippe Ciuciu, Alexandre Gramfort, Leila Azizi, Virginie van Wassenhove
License	CC0
Citation / DOI	10.18112/openneuro.ds003392.v1.0.4
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: 11
Recordings: 159
Tasks: 2

Channels & sampling rate

Channels: 306 (22), 320 (22)
Sampling rate (Hz): 2000.0
Duration (hours): 0.0

API Reference#

Use the DS003392 class to access this dataset programmatically.

class eegdash.dataset.DS003392(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003392. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 12; recordings: 33; 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/ds003392 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003392

Examples

>>> from eegdash.dataset import DS003392
>>> dataset = DS003392(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