DS007353: eeg, meg dataset, 32 subjects#
HAD-MEEG
Citation: Guohao Zhang, Sai Ma, Ming Zhou, Shaohua Tang, Shuyi Zhen, Zheng Li, Zonglei Zhen (—). HAD-MEEG. 10.18112/openneuro.ds007353.v1.0.0
32-participant EEG, MEG dataset — HAD-MEEG.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS007353
dataset = DS007353(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS007353(cache_dir="./data", subject="01")
Advanced query
dataset = DS007353(
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{ds007353,
title = {HAD-MEEG},
author = {Guohao Zhang and Sai Ma and Ming Zhou and Shaohua Tang and Shuyi Zhen and Zheng Li and Zonglei Zhen},
doi = {10.18112/openneuro.ds007353.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds007353.v1.0.0},
}
About This Dataset#
Human action recognition is a core component of social cognition, engaging spatially distributed and temporally evolving neural responses that encode visual information and infer intention. To map the brain’s spatial organization supporting this process, we previously released the Human Action Dataset (HAD), a functional magnetic resonance imaging (fMRI) resource. However, fMRI’s limited temporal resolution constrains its ability to capture rapid neural dynamics. Here, we present the HAD-MEEG dataset, which extends HAD-fMRI, leveraging the millisecond-level temporal resolution of magnetoencephalography (MEG) and electroencephalography (EEG). HAD-MEEG were recorded in the same participants and with the same stimuli as HAD-fMRI, in which 30 participants viewed 21,600 video clips spanning 180 categories of human action. By integrating the temporal precision of M/EEG with the spatial precision of fMRI, HAD enables comprehensive spatiotemporal investigation of the neural mechanisms underlying human action recognition.
Cohort#
Dataset Statistics#
Age distribution by gender (n=31, range 18–31 yr, mean 22.5 yr)
Sex composition
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 44 h
Signal · Electrodes & live trace#
Live trace viewer — sub-09 · ses-eeg · task-action · run-06
Showing one representative recording out of
32 subjects and 473 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — MEG · 273 sensors — 273 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
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 |
HAD-MEEG |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Guohao Zhang, Sai Ma, Ming Zhou, Shaohua Tang, Shuyi Zhen, Zheng Li, Zonglei Zhen |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds007353,
title = {HAD-MEEG},
author = {Guohao Zhang and Sai Ma and Ming Zhou and Shaohua Tang and Shuyi Zhen and Zheng Li and Zonglei Zhen},
doi = {10.18112/openneuro.ds007353.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds007353.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS007353 · Zhang2026eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS007353(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
HAD-MEEG
- Study:
ds007353(OpenNeuro)- Author (year):
Zhang2026- Canonical:
—
Also importable as:
DS007353,Zhang2026.Modality:
eeg, meg; Experiment type:Perception; Subject type:Healthy. Subjects: 32; recordings: 473; 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
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/ds007353 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007353 DOI: https://doi.org/10.18112/openneuro.ds007353.v1.0.0
Examples
>>> from eegdash.dataset import DS007353 >>> dataset = DS007353(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.pytorch
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 ds007353 to reproduce the tutorial on this dataset.
Citation
Guohao Zhang, Sai Ma, Ming Zhou, Shaohua Tang, Shuyi Zhen, … (n.d.). HAD-MEEG. 10.18112/openneuro.ds007353.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds007353.v1.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset