DS003478: eeg dataset, 122 subjects#
EEG: Depression rest
Citation: James F Cavanagh jcavanagh@unm.edu (20). EEG: Depression rest. 10.18112/openneuro.ds003478.v1.1.0
122-participant EEG dataset — EEG: Depression rest.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003478
dataset = DS003478(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003478(cache_dir="./data", subject="01")
Advanced query
dataset = DS003478(
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{ds003478,
title = {EEG: Depression rest},
author = {James F Cavanagh jcavanagh@unm.edu},
doi = {10.18112/openneuro.ds003478.v1.1.0},
url = {https://doi.org/10.18112/openneuro.ds003478.v1.1.0},
}
About This Dataset#
Resting EEG data with 122 college-age participants. These are the same participants as the Openneuro prob selection task. Subjects have the same task IDs, so you could match them up if you like. Task included in DMDX programming language, with instructions for eyes open & eyes closed Triggers included for instrucgted one minute spans for open or closed, e.g. : OCCOCO or COOCOC Data collected circa 2008-2010 in John J.B. Allen lab at U Arizona. Subjects scored reliably high or low in Beck Depression Inventory. Some have been clinically interviewed. See .xls sheet. For some subjects (maybe all?), HEOG and VEOG may be mis-labeled as the other. Some files have had some channels interpolated already. There are no raw data to revert to instead… I have never even looked at the last rest run; no idea how it looks. First rest run was high quality though. The first 6 mins happened immedately after EEG hook-up. The second 6 minutes came after task performance (about 1 hour later) 516 has no rest2. 544 was unused in all anlayses due to unstable BDI between mass assessment and lab assessment (1-4 months) - James F Cavanagh 01/18/2021
Cohort#
Dataset Statistics#
Age distribution by gender (n=120, range 18–24 yr, mean 18.9 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 500.0 Hz (n=243 recordings)
Total recording duration: 23 h 21 min
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · task-Rest · run-02
Showing one representative recording out of
122 subjects and 243 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: Depression rest |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
James F Cavanagh jcavanagh@unm.edu |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003478,
title = {EEG: Depression rest},
author = {James F Cavanagh jcavanagh@unm.edu},
doi = {10.18112/openneuro.ds003478.v1.1.0},
url = {https://doi.org/10.18112/openneuro.ds003478.v1.1.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003478 · Cavanagh2021_Depressioneegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003478(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
EEG: Depression rest
- Study:
ds003478(OpenNeuro)- Author (year):
Cavanagh2021_Depression- Canonical:
—
Also importable as:
DS003478,Cavanagh2021_Depression.Modality:
eeg. Subjects: 122; recordings: 243; 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/ds003478 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003478 DOI: https://doi.org/10.18112/openneuro.ds003478.v1.1.0 NEMAR citation count: 22
Examples
>>> from eegdash.dataset import DS003478 >>> dataset = DS003478(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/ds003478").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 ds003478 to reproduce the tutorial on this dataset.
Citation
James F Cavanagh jcavanagh@unm.edu (20). EEG: Depression rest. 10.18112/openneuro.ds003478.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.ds003478.v1.1.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset