EEGdash›OpenNeuro›DS007640

Iss. 7640 · 23 subjects · 94 recordings · CC0

Dataset Brief · Dataset of emotion recognition using validated video stimuli…

DS007640: meg dataset, 23 subjects#

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

Citation: Moon-A Yoo, Dong-Uk Kim, Soo-In Choi, Min-Young Kim, Sung-Phil Kim (—). Dataset of emotion recognition using validated video stimuli with large-scale behavioral survey and MEG recordings. 10.18112/openneuro.ds007640.v1.0.1

23-participant MEG dataset — Dataset of emotion recognition using validated video stimuli with large-scale behavioral survey and MEG recordings.

Data & curation Moon-A Yoo · Dong-Uk Kim · Soo-In Choi · Min-Young Kim · Sung-Phil Kim

1024 HzBIDS TBD4 tasks4 sessions

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

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.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007640.v1.0.1},
}

§ 02Study · The README

About This Dataset#

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:

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.
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.
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.

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

General Description

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] In addition, please cite the OpenNeuro dataset itself using its DOI: doi:10.18112/openneuro.ds007640.v1.0.0

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution (n=23, range 21–40 yr, mean 26.7 yr · sex per subject not reported)

20253040

Sex composition

Female
12
Male
11
F : M ratio
1.09 : 1

52% female · n = 23 subjects with reported sex.

Sampling frequencies: 1024.0 Hz (n=94 recordings)

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage — ch · MEG · 1024 Hz · 23 subjects, 94 recordings

Live trace viewer — sub-04 · ses-01 · task-HAHV

Showing one representative recording out of 23 subjects and 94 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _meg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?meg=<url>) to inspect it.

Electrode layout — MEG · 142 sensors — 142 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

§ 05Manifest · BIDS tree

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS007640(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asDS007640

Sourceeegdash/dataset/registry.py · [source ↗]

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

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.v1.0.1

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)

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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS007640.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

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 ds007640 to reproduce the tutorial on this dataset.

Citation

Moon-A Yoo, Dong-Uk Kim, Soo-In Choi, Min-Young Kim, Sung-Phil Kim (n.d.). Dataset of emotion recognition using validated video stimuli with large-scale behavioral survey and MEG recordings. 10.18112/openneuro.ds007640.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds007640.v1.0.1.

Related & sibling datasets

DS007663MEG · 27 subj ON000117MEG · 17 subj ON005261MEG · 17 subj DS004012MEG · 30 subj DS003694MEG · 28 subj

+ 1 more — see See Also below →

BIDS

BIDS TBD

Sidecars

not yet probed

Provenance

CC0 · 10.18112/openneuro.ds007640.v1.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

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	—
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.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS007640: meg dataset, 23 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#