DS007353: eeg, meg dataset, 32 subjects#

HAD-MEEG

Access recordings and metadata through EEGDash.

Citation: Guohao Zhang, Sai Ma, Ming Zhou, Shaohua Tang, Shuyi Zhen, Zheng Li, Zonglei Zhen (2026). HAD-MEEG. 10.18112/openneuro.ds007353.v1.0.0

Modality: eeg, meg Subjects: 32 Recordings: 473 License: CC0 Source: openneuro

Metadata: Complete (100%)

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.

Dataset Information#

Dataset ID

DS007353

Title

HAD-MEEG

Author (year)

Zhang2026

Canonical

HAD_MEEG, HADMEEG

Importable as

DS007353, Zhang2026, HAD_MEEG, HADMEEG

Year

2026

Authors

Guohao Zhang, Sai Ma, Ming Zhou, Shaohua Tang, Shuyi Zhen, Zheng Li, Zonglei Zhen

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007353.v1.0.0

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},
}

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: 32

  • Recordings: 473

  • Tasks: 2

Channels & sampling rate

  • Channels: 409 (240), 64 (224), 378 (9)

  • Sampling rate (Hz): 1200.0 (249), 1000.0 (224)

  • Duration (hours): 44.82657291666667

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Perception

Files & format

  • Size on disk: 180.6 GB

  • File count: 473

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007353.v1.0.0

Provenance

API Reference#

Use the DS007353 class to access this dataset programmatically.

class eegdash.dataset.DS007353(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

HAD-MEEG

Study:

ds007353 (OpenNeuro)

Author (year):

Zhang2026

Canonical:

HAD_MEEG, HADMEEG

Also importable as: DS007353, Zhang2026, HAD_MEEG, HADMEEG.

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

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/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, 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#