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#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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

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

Electrode Layout#

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

Dataset Statistics#

Age distribution (n=23, range 21–40 yr)

20253040

Sex distribution

12

11

Female Male Total: 23

Sampling frequencies: 1024.0 Hz (n=94 recordings)

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

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: 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)