DS005114#

EEG: DPX Cog Ctl Task in Acute Mild TBI

Access recordings and metadata through EEGDash.

Citation: James F Cavanagh (2024). EEG: DPX Cog Ctl Task in Acute Mild TBI. 10.18112/openneuro.ds005114.v1.0.0

Modality: eeg Subjects: 91 Recordings: 1796 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005114

dataset = DS005114(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 = DS005114(cache_dir="./data", subject="01")

Advanced query

dataset = DS005114(
    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{ds005114,
  title = {EEG: DPX Cog Ctl Task in Acute Mild TBI},
  author = {James F Cavanagh},
  doi = {10.18112/openneuro.ds005114.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005114.v1.0.0},
}

About This Dataset#

Dot Probe Continuous Performance Task in control & sub-acute mild TBI. Published here: https://pubmed.ncbi.nlm.nih.gov/31368085/ 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 (bug issues on upload). Session 2 was ~2 months (1.5 to 3) and Session 3 was ~4 months (3 to 5) following Session 1. 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/ 10.1016/j.neuropsychologia.2019.107125 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. - James F Cavanagh 04/29/2024

Dataset Information#

Dataset ID	`DS005114`
Title	EEG: DPX Cog Ctl Task in Acute Mild TBI
Year	2024
Authors	James F Cavanagh
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005114.v1.0.0
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: 91
Recordings: 1796
Tasks: 1

Channels & sampling rate

Channels: 64 (229), 65 (217)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS005114 class to access this dataset programmatically.

class eegdash.dataset.DS005114(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005114. Modality: eeg; Experiment type: Attention; Subject type: TBI. Subjects: 91; recordings: 223; 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/ds005114 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005114

Examples

>>> from eegdash.dataset import DS005114
>>> dataset = DS005114(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