EEGdash›NeMAR›ON008257

Iss. 8257 · 6 subjects · 768 recordings · CC0

Dataset Brief · EEG Moments Dataset (EMD)

ON008257: eeg dataset, 6 subjects#

EEG Moments Dataset (EMD)

Access recordings and metadata through EEGDash.

Citation: Alessandro T. Gifford, Pablo Oyarzo, Anne W. Zonneveld, Christina Sartzetaki, Iris I.A. Groen, Radoslaw M. Cichy (20). EEG Moments Dataset (EMD). 10.82901/nemar.on008257

Modality: eeg Subjects: 6 Recordings: 768 License: CC0 Source: nemar

Metadata: Complete (100%)

6-participant EEG dataset — EEG Moments Dataset (EMD).

Data & curation Alessandro T. Gifford · Pablo Oyarzo · Anne W. Zonneveld · Christina Sartzetaki · Iris I.A. Groen · Radoslaw M. Cichy
Senior author Radoslaw M. Cichy
Year 20 · Distributed via NeMAR

EEG · 128 ch1000 HzBIDS 1.9.0Task · video8 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 ON008257

dataset = ON008257(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 = ON008257(cache_dir="./data", subject="01")

Advanced query

dataset = ON008257(
    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{on008257,
  title = {EEG Moments Dataset (EMD)},
  author = {Alessandro T. Gifford and Pablo Oyarzo and Anne W. Zonneveld and Christina Sartzetaki and Iris I.A. Groen and Radoslaw M. Cichy},
  doi = {10.82901/nemar.on008257},
  url = {https://doi.org/10.82901/nemar.on008257},
}

§ 02Study · The README

About This Dataset#

This is the data repository for the EEG Moments Dataset (EMD). EMD contains EEG responses to 1,102 3-second videos across 6 human subjects. During the EMD experiment, the video stimuli were presented with the corresponding audio track, so as to enable analyses of visual and/or auditory processing of naturalistic dynamic events. Each subject saw the 1,000 video training set 6 times, and the 102 video testing set 24 times. Each video is additionally human-annotated with 15 object labels, 5 scene labels, 5 action labels, 5 sentence text descriptions, 1 spoken transcription, 1 memorability score, and 1 memorability decay rate.

EMD is the EEG companion dataset of the BOLD Moments Dataset (BMD), which consists of fMRI responses for the same 1,102 3-second videos. EMD additionally contains eye tracking data (gaze and pupil size) collected during the EEG experiment. Note that subjects were instructed to maintain central fixation during the stimulus video presentation.

The home folder (everything except the ./stimuli and the ./derivatives/ folders) contains the raw EEG and eye-tracking data in BIDS format before any preprocessing. The eye-tracking data is denoted as physio in the corresponding file names. Download this folder if you want to run your own preprocessing pipeline.

DOI

EEG Moments Dataset (EMD)

The ./stimuli/ folder contains a .txt file with instructions to access the video stimuli which, due to copyright permission considerations, must be downloaded separately. The ./derivatives/ folder contains all data derivatives, including the stimulus metadata (./derivatives/stimuli_metadata/), preprocessed EEG data (./derivatives/eeg/), and preprocessed eye-tracking data (./derivatives/eyetracking/).

📝 Data collection notes

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=6, range 21–32 yr, mean 24.8 yr)

202530

Female · 3Male · 3

Sex composition

Female
3
Male
3
F : M ratio
1.00 : 1

50% female · n = 6 subjects with reported sex.

Channel counts: 128 ch (n=768 recordings)

Sampling frequencies: 1000.0 Hz (n=768 recordings)

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 128 ch · EEG · 1000 Hz · 6 subjects, 768 recordings

Live trace viewer — sub-05 · ses-01 · task-video · run-13

Showing one representative recording out of 6 subjects and 768 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.

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

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.ON008257(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asON008257

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

class eegdash.dataset.ON008257(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

EEG Moments Dataset (EMD)

Study:: on008257 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: ON008257.

Modality: eeg; Subject type: Unknown. Subjects: 6; recordings: 768; tasks: 1.

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/on008257 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on008257 DOI: https://doi.org/10.82901/nemar.on008257

Examples

>>> from eegdash.dataset import ON008257
>>> dataset = ON008257(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 — ON008257.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 on008257 to reproduce the tutorial on this dataset.

Citation

Alessandro T. Gifford, Pablo Oyarzo, Anne W. Zonneveld, Christina Sartzetaki, Iris I.A. Groen, … (20). EEG Moments Dataset (EMD). 10.82901/nemar.on008257

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.on008257.

Related & sibling datasets

NM000232EEG · 10 subj NM000203EEG · 13 subj ON004952EEG · 10 subj ON004447EEG · 22 subj ON004444EEG · 30 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events

Provenance

CC0 · 10.82901/nemar.on008257

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`ON008257`
Title	EEG Moments Dataset (EMD)
Author (year)	—
Canonical	—
Importable as	`ON008257`
Year	20
Authors	Alessandro T. Gifford, Pablo Oyarzo, Anne W. Zonneveld, Christina Sartzetaki, Iris I.A. Groen, Radoslaw M. Cichy
License	CC0
Citation / DOI	10.82901/nemar.on008257
Source links	OpenNeuro \| NeMAR \| Source URL

ON008257: eeg dataset, 6 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#