DS007640: meg dataset, 23 subjects#

Dataset of emotion recognition using validated video stimuli with large-scale behavioral survey and MEG recordings

Access recordings and metadata through EEGDash.

Citation: Moon-A Yoo, Dong-Uk Kim, Soo-In Choi, Min-Young Kim, Sung-Phil Kim (2026). Dataset of emotion recognition using validated video stimuli with large-scale behavioral survey and MEG recordings. 10.18112/openneuro.ds007640.v1.0.0

Modality: meg Subjects: 23 Recordings: 94 License: CC0 Source: openneuro

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007640

dataset = DS007640(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007640(cache_dir="./data", subject="01")

Advanced query

dataset = DS007640(
    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{ds007640,
  title = {Dataset of emotion recognition using validated video stimuli with large-scale behavioral survey and MEG recordings},
  author = {Moon-A Yoo and Dong-Uk Kim and Soo-In Choi and Min-Young Kim and Sung-Phil Kim},
  doi = {10.18112/openneuro.ds007640.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007640.v1.0.0},
}

About This Dataset#

Dataset of Emotion Recognition Using Validated Video Stimuli with Large-scale Behavioral Survey and MEG Recordings

General Description

This dataset was developed as part of research focused on brain signal-based emotion recognition by capturing high-fidelity Magnetoencephalography (MEG) signals during induced different emotional states. The dataset is organized into three primary components: 1. Phenotype (Online Survey): Stored within the ‘phenotype’ directory, this component contains the results of a large-scale subjective emotional assessment of the set of 40 video stimuli. It includes responses from 500 participants, providing robust behavioral baseline. 2. Source Data (Head Digitization): Located in ‘sourcedata’ directory, this component contains head position information measured prior to each recording session. The resulting configuration files (‘.cfg’) store the 3D spatial coordinates (x, y, z) for anatomical landmarks and Head Position Indicator (HPI) coils, essential for accurate co-registration. 3. MEG Recording: Comprehensive MEG neural recordings from 23 participants, who viewed the same 40 validated video clips used in the online survey. These recordings enable the investigation of emotion-specific neural signal patterns and are organized into subject-specific directories (e.g., ‘sub-01’).

To capture the richness of emotional experiences, we employed a multi-faceted assessment paradigm. Beyond the standard Self-Assessment Manikin (SAM) responses for Valence and Arousal, our labels include discrete emotion categories (PrEmo) and temporal highlight scene selections, providing a more granular ‘ground truth’ for affective states.

Citation

For a detailed description of the stimulus selection, experimental design, and data acquisition process, please refer to the publication listed below. We kindly request that any research utilizing this dataset cites the following paper: [Add on Reference/DOI]

Dataset Information#

Dataset ID

DS007640

Title

Dataset of emotion recognition using validated video stimuli with large-scale behavioral survey and MEG recordings

Author (year)

—

Canonical

—

Importable as

DS007640

Year

2026

Authors

Moon-A Yoo, Dong-Uk Kim, Soo-In Choi, Min-Young Kim, Sung-Phil Kim

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007640.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007640,
  title = {Dataset of emotion recognition using validated video stimuli with large-scale behavioral survey and MEG recordings},
  author = {Moon-A Yoo and Dong-Uk Kim and Soo-In Choi and Min-Young Kim and Sung-Phil Kim},
  doi = {10.18112/openneuro.ds007640.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007640.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: 23

  • Recordings: 94

  • Tasks: 4

Channels & sampling rate

  • Channels: Varies

  • Sampling rate (Hz): 1024.0

  • Duration (hours): Not calculated

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 88.8 GB

  • File count: 94

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007640.v1.0.0

Provenance

API Reference#

Use the DS007640 class to access this dataset programmatically.

class eegdash.dataset.DS007640(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Dataset of emotion recognition using validated video stimuli with large-scale behavioral survey and MEG recordings

Study:

ds007640 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007640, nan.

Modality: meg. Subjects: 23; recordings: 94; tasks: 4.

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/ds007640 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007640 DOI: https://doi.org/10.18112/openneuro.ds007640.v1.0.0

Examples

>>> from eegdash.dataset import DS007640
>>> dataset = DS007640(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#