DS003523: eeg dataset, 91 subjects#
EEG: Visual Working Memory in Acute TBI
Citation: James F Cavanagh (20). EEG: Visual Working Memory in Acute TBI. 10.18112/openneuro.ds003523.v1.1.0
91-participant EEG dataset — EEG: Visual Working Memory in Acute TBI.
Quickstart#
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)
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'])
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
Cohort#
Dataset Statistics#
Age distribution by gender (n=91, range 18–55 yr, mean 30.0 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 500.0 Hz (n=221 recordings)
Total recording duration: 84 h
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · ses-02 · task-VisualWorkingMemory
Showing one representative recording out of
91 subjects and 221 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.
Electrode layout — EEG · 64 sensors — 64 channels
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
Manifest#
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.
Full dataset metadata table
Dataset ID |
|
Title |
EEG: Visual Working Memory in Acute TBI |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
James F Cavanagh |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003523 · Cavanagh2021_Visual_Workingeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003523(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
EEG: Visual Working Memory in Acute TBI
- Study:
ds003523(OpenNeuro)- Author (year):
Cavanagh2021_Visual_Working- Canonical:
—
Also importable as:
DS003523,Cavanagh2021_Visual_Working.Modality:
eeg. 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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 DOI: https://doi.org/10.18112/openneuro.ds003523.v1.1.0 NEMAR citation count: 3
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: 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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds003523").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds003523 to reproduce the tutorial on this dataset.
Citation
James F Cavanagh (20). EEG: Visual Working Memory in Acute TBI. 10.18112/openneuro.ds003523.v1.1.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003523.v1.1.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset