DS004212: meg dataset, 5 subjects#
THINGS-MEG
Citation: Martin N. Hebart, Oliver Contier, Lina Teichmann, Adam H. Rockter, Charles Zheng, Alexis Kidder, Anna Corriveau, Maryam Vaziri-Pashkam, Chris I. Baker (20). THINGS-MEG. 10.18112/openneuro.ds004212.v3.0.0
5-participant MEG dataset — THINGS-MEG.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004212
dataset = DS004212(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004212(cache_dir="./data", subject="01")
Advanced query
dataset = DS004212(
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{ds004212,
title = {THINGS-MEG},
author = {Martin N. Hebart and Oliver Contier and Lina Teichmann and Adam H. Rockter and Charles Zheng and Alexis Kidder and Anna Corriveau and Maryam Vaziri-Pashkam and Chris I. Baker},
doi = {10.18112/openneuro.ds004212.v3.0.0},
url = {https://doi.org/10.18112/openneuro.ds004212.v3.0.0},
}
About This Dataset#
Understanding object representations visual and semantic processing of objects requires a broad, comprehensive sampling of the objects in our visual world
with dense measurements of brain activity and behavior. This densely sampled fMRI dataset is part of THINGS-data, a multimodal collection of large-scale datasets comprising functional MRI, magnetoencephalographic recordings, and 4.70 million behavioral judgments in response to thousands of photographic images for up to 1,854 object concepts. THINGS-data is unique in its breadth of richly-annotated objects, allowing for testing countless novel hypotheses at scale while assessing the reproducibility of previous findings. The multimodal data allows for studying both the temporal and spatial dynamics of object representations and their relationship to behavior and additionally provides the means for combining these datasets for novel insights into object processing. THINGS-data constitutes the core release of the THINGS initiative for bridging the gap between disciplines and the advancement of cognitive neuroscience.
We collected extensively sampled object representations using magnetoencephalography (MEG). To this end, we drew on the THINGS database (Hebart et al., 2019),
a richly-annotated database of 1,854 object concepts representative of the American English language which contains 26,107 manually-curated naturalistic object images.
THINGS-MEG
During the fMRI experiment, participants were shown a representative subset of THINGS images, spread across 12 separate sessions (N=4, 22,448 unique images of 1,854 objects). Images were shown in fast succession (1.5±0.2s), and participants were instructed to maintain central fixation. To ensure engagement, participants performed an oddball detection task responding to occasional artificially-generated images. A subset of images (n=200) were shown repeatedly in each session.
Beyond the core functional imaging data in response to THINGS images, we acquired T1-weighted MRI scans to allow for cortical source localization. Eye movements were monitored in the MEG to ensure participants maintained central fixation.
Cohort#
Dataset Statistics#
Sex composition
Channel counts: 310 ch (n=470 recordings)
Sampling frequencies: 1200.0 Hz (n=470 recordings)
Total recording duration: 45 h
Signal · Electrodes & live trace#
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.
HED event descriptors word cloud
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.
Full dataset metadata table
Dataset ID |
|
Title |
THINGS-MEG |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Martin N. Hebart, Oliver Contier, Lina Teichmann, Adam H. Rockter, Charles Zheng, Alexis Kidder, Anna Corriveau, Maryam Vaziri-Pashkam, Chris I. Baker |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004212,
title = {THINGS-MEG},
author = {Martin N. Hebart and Oliver Contier and Lina Teichmann and Adam H. Rockter and Charles Zheng and Alexis Kidder and Anna Corriveau and Maryam Vaziri-Pashkam and Chris I. Baker},
doi = {10.18112/openneuro.ds004212.v3.0.0},
url = {https://doi.org/10.18112/openneuro.ds004212.v3.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004212 · Hebart2022eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004212(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
THINGS-MEG
- Study:
ds004212(OpenNeuro)- Author (year):
Hebart2022- Canonical:
—
Also importable as:
DS004212,Hebart2022.Modality:
meg; Experiment type:Perception; Subject type:Healthy. Subjects: 5; recordings: 500; tasks: 1.- 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds004212 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004212 DOI: https://doi.org/10.18112/openneuro.ds004212.v3.0.0 NEMAR citation count: 3
Examples
>>> from eegdash.dataset import DS004212 >>> dataset = DS004212(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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds004212").huggingface
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 ds004212 to reproduce the tutorial on this dataset.
Citation
Martin N. Hebart, Oliver Contier, Lina Teichmann, Adam H. Rockter, Charles Zheng, … (20). THINGS-MEG. 10.18112/openneuro.ds004212.v3.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004212.v3.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset