DS004784#

Phantom EEG Dataset with Motion, Muscle, and Eye Artifacts and Example Scripts

Access recordings and metadata through EEGDash.

Citation: Ryan J. Downey, Daniel P. Ferris (2023). Phantom EEG Dataset with Motion, Muscle, and Eye Artifacts and Example Scripts. 10.18112/openneuro.ds004784.v1.0.4

Modality: eeg Subjects: 1 Recordings: 51 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004784

dataset = DS004784(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004784(cache_dir="./data", subject="01")

Advanced query

dataset = DS004784(
    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{ds004784,
  title = {Phantom EEG Dataset with Motion, Muscle, and Eye Artifacts and Example Scripts},
  author = {Ryan J. Downey and Daniel P. Ferris},
  doi = {10.18112/openneuro.ds004784.v1.0.4},
  url = {https://doi.org/10.18112/openneuro.ds004784.v1.0.4},
}

About This Dataset#

This phantom experiment contains data collected from a an electrically conductive head phantom. Six conditions were tested: brain-only [no artifacts], or brain with eye, jaw muscle, neck muscle, or motion artifacts present, or brain with all artifacts simultaneously present. Also contained is a copy of the iCanClean plugin for EEGLAB and a set of other helpful scripts that enable parameter sweep testing and validation with ground truth knowledge of the brain signals of interest. Please see derivatives folder and read the How To document within. A copy of iCanClean plugin is in derivatives->Scripts->plugins Please see reference for methodological details https://doi.org/10.3390/s23198214

  • Ryan Downey (December 20, 2023)

Dataset Information#

Dataset ID

DS004784

Title

Phantom EEG Dataset with Motion, Muscle, and Eye Artifacts and Example Scripts

Year

2023

Authors

Ryan J. Downey, Daniel P. Ferris

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004784.v1.0.4

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004784,
  title = {Phantom EEG Dataset with Motion, Muscle, and Eye Artifacts and Example Scripts},
  author = {Ryan J. Downey and Daniel P. Ferris},
  doi = {10.18112/openneuro.ds004784.v1.0.4},
  url = {https://doi.org/10.18112/openneuro.ds004784.v1.0.4},
}

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: 1

  • Recordings: 51

  • Tasks: 1

Channels & sampling rate

  • Channels: 128 (6), 264 (6)

  • Sampling rate (Hz): 512.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 1.0 GB

  • File count: 51

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004784.v1.0.4

Provenance

API Reference#

Use the DS004784 class to access this dataset programmatically.

class eegdash.dataset.DS004784(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004784. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 1; recordings: 6; tasks: 6.

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/ds004784 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004784

Examples

>>> from eegdash.dataset import DS004784
>>> dataset = DS004784(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

See Also#