DS003523#

EEG: Visual Working Memory in Acute TBI

Access recordings and metadata through EEGDash.

Citation: James F Cavanagh (2021). EEG: Visual Working Memory in Acute TBI. 10.18112/openneuro.ds003523.v1.1.0

Modality: eeg Subjects: 91 Recordings: 1802 License: CC0 Source: openneuro Citations: 3.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003523

dataset = DS003523(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 = DS003523(cache_dir="./data", subject="01")

Advanced query

dataset = DS003523(
    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{ds003523,
  title = {EEG: Visual Working Memory in Acute TBI},
  author = {James F Cavanagh},
  doi = {10.18112/openneuro.ds003523.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds003523.v1.1.0},
}

About This Dataset#

Visual working memory in control & sub-acute mild TBI. Mind wandering probes were inserted between trials. **DATA NEVER PUBLISHED!**If youre interested in working together, I have 1/3 of the paper already done, including CONSORT diagrams, tables, behavioral analysis, etc. All EEG data are even fully cleaned and pre-processed. 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. 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/ 10.1016/j.neuropsychologia.2019.107125 Same task as this one here: 10.3758/s13415-018-0584-6. 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 02/17/2021

Dataset Information#

Dataset ID	`DS003523`
Title	EEG: Visual Working Memory in Acute TBI
Year	2021
Authors	James F Cavanagh
License	CC0
Citation / DOI	10.18112/openneuro.ds003523.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: 91
Recordings: 1802
Tasks: 1

Channels & sampling rate

Channels: 64 (226), 65 (216)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS003523 class to access this dataset programmatically.

class eegdash.dataset.DS003523(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003523. Modality: eeg; Experiment type: Memory; Subject type: TBI. Subjects: 91; recordings: 221; 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/ds003523 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003523

Examples

>>> from eegdash.dataset import DS003523
>>> dataset = DS003523(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