DS004784: eeg dataset, 1 subjects#

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: 6 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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
Author (year)	`Downey2023`
Canonical	—
Importable as	`DS004784`, `Downey2023`
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

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: 6
Tasks: 6

Channels & sampling rate

Channels: 264
Sampling rate (Hz): 512.0
Duration (hours): 0.5408333333333334

Electrode Layout#

Electrode layout — EEG · 128 sensors — 128 channels

Dataset Statistics#

Channel counts: 264 ch (n=6 recordings)

Sampling frequencies: 512.0 Hz (n=6 recordings)

Total recording duration: 32 min

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

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

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

Study:: ds004784 (OpenNeuro)
Author (year):: Downey2023
Canonical:: —

Also importable as: DS004784, Downey2023.

Modality: eeg. 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 DOI: https://doi.org/10.18112/openneuro.ds004784.v1.0.4 NEMAR citation count: 1

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: 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)