DS003522: eeg dataset, 96 subjects#

EEG: Three-Stim Auditory Oddball and Rest in Acute and Chronic TBI

Access recordings and metadata through EEGDash.

Citation: James F Cavanagh, Davin Quinn (2021). EEG: Three-Stim Auditory Oddball and Rest in Acute and Chronic TBI. 10.18112/openneuro.ds003522.v1.1.0

Modality: eeg Subjects: 96 Recordings: 200 License: CC0 Source: openneuro Citations: 5.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003522

dataset = DS003522(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS003522(cache_dir="./data", subject="01")

Advanced query

dataset = DS003522(
    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{ds003522,
  title = {EEG: Three-Stim Auditory Oddball and Rest in Acute and Chronic TBI},
  author = {James F Cavanagh and Davin Quinn},
  doi = {10.18112/openneuro.ds003522.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds003522.v1.1.0},
}

About This Dataset#

3 stimulus auditory oddball data in control, sub-acute mild TBI, and chronic TBI. Rest data is also included. 3AOB data published here: 10.1016/j.neuropsychologia.2019.107125. FYI, same task as this different dataset: https://openneuro.org/datasets/ds003490/versions/1.1.0. For CTL and sub-acute mTBI: Session 1 was from 3 to 14 days post-injury and was the only session with MRI. (MRI will be uploaded …later). Session 2 was ~2 months (1.5 to 3) and Session 3 was ~4 months (3 to 5) following Session 1. For Chronic TBI, there was only one session for this study. There was A LOT of subject attrition over timepoints. Same samples as reported here: https://psycnet.apa.org/record/2020-66677-001 https://pubmed.ncbi.nlm.nih.gov/31344589/ https://pubmed.ncbi.nlm.nih.gov/31368085/ Task included in Matlab programming language. Data collected 2016-2018 in the Center for Brain Recovery and Repair at the UNM Health Sciences Center. Check the .xls sheet under code folder for LOTS more meta data. Analysis scripts are included to re-create the paper. - James F Cavanagh 02/17/2021

Dataset Information#

Dataset ID

DS003522

Title

EEG: Three-Stim Auditory Oddball and Rest in Acute and Chronic TBI

Author (year)

Cavanagh2021_Three_Stim

Canonical

—

Importable as

DS003522, Cavanagh2021_Three_Stim

Year

2021

Authors

James F Cavanagh, Davin Quinn

License

CC0

Citation / DOI

10.18112/openneuro.ds003522.v1.1.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds003522,
  title = {EEG: Three-Stim Auditory Oddball and Rest in Acute and Chronic TBI},
  author = {James F Cavanagh and Davin Quinn},
  doi = {10.18112/openneuro.ds003522.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds003522.v1.1.0},
}

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: 96

  • Recordings: 200

  • Tasks: 1

Channels & sampling rate

  • Channels: 65 (192), 64 (8)

  • Sampling rate (Hz): 500.0

  • Duration (hours): 57.07904611111111

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 25.4 GB

  • File count: 200

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: 10.18112/openneuro.ds003522.v1.1.0

Provenance

Electrode Layout#

Electrode layout — EEG · 64 sensors — 64 channels

Dataset Statistics#

Age distribution (n=96, range 18–55 yr)

152025303540455055

Sex distribution

96
Other  Total: 96

Channel counts (ch)

6465

Sampling frequencies: 500.0 Hz (n=200 recordings)

Total recording duration: 57 h

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 — DS003522
HED event descriptors word cloud HED event descriptors word cloud — DS003522

Signal Preview#

Live trace viewer — sub-021 · ses-02 · task-ThreeStimAuditoryOddball

Showing one representative recording out of 96 subjects and 200 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.

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 DS003522 class to access this dataset programmatically.

class eegdash.dataset.DS003522(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

EEG: Three-Stim Auditory Oddball and Rest in Acute and Chronic TBI

Study:

ds003522 (OpenNeuro)

Author (year):

Cavanagh2021_Three_Stim

Canonical:

—

Also importable as: DS003522, Cavanagh2021_Three_Stim.

Modality: eeg. Subjects: 96; recordings: 200; 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/ds003522 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003522 DOI: https://doi.org/10.18112/openneuro.ds003522.v1.1.0 NEMAR citation count: 5

Examples

>>> from eegdash.dataset import DS003522
>>> dataset = DS003522(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.

See Also#