EEGdash›OpenNeuro›DS006768
Iss. 6768 · 30 subjects · 210 recordings · CC0
Dataset Brief · Multiple Object Monitoring (EEG)

DS006768: eeg dataset, 30 subjects#

Multiple Object Monitoring (EEG)

Citation: Benjamin G. Lowe, Alexandra Woolgar, Sophie Smit, Anina N. Rich (20). Multiple Object Monitoring (EEG). 10.18112/openneuro.ds006768.v1.1.0

30-participant EEG dataset — Multiple Object Monitoring (EEG).

Data & curation Benjamin G. Lowe · Alexandra Woolgar · Sophie Smit · Anina N. Rich
Year 20 · Distributed via OpenNeuro
EEG · 64 ch1000 HzBIDS 1.11.0Task · MOMHealthyVisualAttention
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006768

dataset = DS006768(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS006768(cache_dir="./data", subject="01")

Advanced query

dataset = DS006768(
    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{ds006768,
  title = {Multiple Object Monitoring (EEG)},
  author = {Benjamin G. Lowe and Alexandra Woolgar and Sophie Smit and Anina N. Rich},
  doi = {10.18112/openneuro.ds006768.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds006768.v1.1.0},
}
§ 02Study · The README

About This Dataset#

Subjects (N = 30) completed a Multiple Object Monitoring (MOM) task. Methodological details can be read within the pre-print: https://doi.org/10.1101/2025.07.10.663816

Please email ben.lowe@mq.edu.au if you have any further questions.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=30, range 17–30 yr, mean 20.2 yr)

15202530
Other · 30

Channel counts: 64 ch (n=210 recordings)

Sampling frequencies: 1000.0 Hz (n=210 recordings)

Total recording duration: 14 h 27 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 ch · EEG · 1000 Hz · 30 subjects, 210 recordings
Live trace viewer — sub-13 · task-MOM · run-02

Showing one representative recording out of 30 subjects and 210 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.

NEMAR pipeline statistics — DS006768
HED event descriptors word cloud HED event descriptors word cloud — DS006768
§ 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…
Full dataset metadata table

Dataset ID

DS006768

Title

Multiple Object Monitoring (EEG)

Author (year)

Lowe2025

Canonical

—

Importable as

DS006768, Lowe2025

Year

20

Authors

Benjamin G. Lowe, Alexandra Woolgar, Sophie Smit, Anina N. Rich

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006768.v1.1.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds006768,
  title = {Multiple Object Monitoring (EEG)},
  author = {Benjamin G. Lowe and Alexandra Woolgar and Sophie Smit and Anina N. Rich},
  doi = {10.18112/openneuro.ds006768.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds006768.v1.1.0},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS006768(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Lowe2025
Canonical—
Importable asDS006768 · Lowe2025
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS006768(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Multiple Object Monitoring (EEG)

Study:

ds006768 (OpenNeuro)

Author (year):

Lowe2025

Canonical:

—

Also importable as: DS006768, Lowe2025.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 30; recordings: 210; 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/ds006768 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006768 DOI: https://doi.org/10.18112/openneuro.ds006768.v1.1.0

Examples

>>> from eegdash.dataset import DS006768
>>> dataset = DS006768(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 FacePre-bundled mirror at EEGDash/ds006768 · pull with datasets.load_dataset("EEGDash/ds006768").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS006768.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 ds006768 to reproduce the tutorial on this dataset.

Citation

Benjamin G. Lowe, Alexandra Woolgar, Sophie Smit, Anina N. Rich (20). Multiple Object Monitoring (EEG). 10.18112/openneuro.ds006768.v1.1.0

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds006768.v1.1.0.

BIDS
BIDS 1.11.0
Sidecars
events
Machine-readable
schema.org/Dataset · Croissant

See Also#