DS006768#

Multiple Object Monitoring (EEG)

Access recordings and metadata through EEGDash.

Citation: Benjamin G. Lowe, Alexandra Woolgar, Sophie Smit, Anina N. Rich (2025). Multiple Object Monitoring (EEG). 10.18112/openneuro.ds006768.v1.1.0

Modality: eeg Subjects: 30 Recordings: 631 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
}

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.

Dataset Information#

Dataset ID	`DS006768`
Title	Multiple Object Monitoring (EEG)
Year	2025
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

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: 30
Recordings: 631
Tasks: 1

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS006768 class to access this dataset programmatically.

class eegdash.dataset.DS006768(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006768. 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

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, 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