DS003944: eeg dataset, 82 subjects#
EEG: First Episode Psychosis vs. Control Resting Task 1
Citation: Dean Salisbury, Dylan Seebold, Brian Coffman (—). EEG: First Episode Psychosis vs. Control Resting Task 1. 10.18112/openneuro.ds003944.v1.0.1
82-participant EEG dataset — EEG: First Episode Psychosis vs. Control Resting Task 1.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003944
dataset = DS003944(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003944(cache_dir="./data", subject="01")
Advanced query
dataset = DS003944(
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{ds003944,
title = {EEG: First Episode Psychosis vs. Control Resting Task 1},
author = {Dean Salisbury and Dylan Seebold and Brian Coffman},
doi = {10.18112/openneuro.ds003944.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds003944.v1.0.1},
}
About This Dataset#
Resting EEG and MEG data was gathered for two independently collected samples of healthy and First Episode Psychosis (FEP) individuals.
To obtain resting data, EEG channels were recorded for 5 minutes using an Elekta Neuromag Vectorview system.
EEG was recorded using a low-impedance 10-10 system 60-channel cap.
The first collected sample of EEG data is provided here. This sample includes a portion of subjects from the second acquisition (EEG: First Episode Psychosis vs. Control Resting Task 2), since they were collected using the same montage. The subjects from Task 2 that have been included here are: sub-2140A, sub-2170A, sub-2174A, sub-2176A, sub-2177A, sub-2184A, sub-2193A, sub-2214A, sub-2217A, sub-2221A.
The phenotype directory contains clinical assessment results and data divided by type for all subjects. The assessment results were categorized as follows:
BPRS - Brief Psychiatric Rating Scale, SANS - Scale for Assessment of Negative Symptoms, SAPS - Scale for Assessment of Positive Symptoms, GAFGAS - Global Assessment of Functioning, SFS - Social Functioning Scale, MATRICS - MATRICS Consensus Cognitive Battery, WASI - Wechsler Abbreviated Scale of Intelligence, Hollingshead - Hollingshead Four-Factor Index of Socioeconomic Status, Medications - Chlorpromazine equivalency of prescribed medication at time of EEG scan.
Values/scores that were not collected and questions without given responses are denoted by n/a.
Cohort#
Dataset Statistics#
Age distribution by gender (n=82, range 13–36 yr, mean 21.5 yr)
Sex composition
Channel counts: 64 ch (n=82 recordings)
Sampling frequencies (Hz)
Total recording duration: 6 h 59 min
Signal · Electrodes & live trace#
Live trace viewer — sub-2002 · task-Rest
Showing one representative recording out of
82 subjects and 82 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 · 61 sensors — 61 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: First Episode Psychosis vs. Control Resting Task 1 |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Dean Salisbury, Dylan Seebold, Brian Coffman |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003944,
title = {EEG: First Episode Psychosis vs. Control Resting Task 1},
author = {Dean Salisbury and Dylan Seebold and Brian Coffman},
doi = {10.18112/openneuro.ds003944.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds003944.v1.0.1},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003944 · Salisbury2021_Firsteegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003944(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
EEG: First Episode Psychosis vs. Control Resting Task 1
- Study:
ds003944(OpenNeuro)- Author (year):
Salisbury2021_First- Canonical:
—
Also importable as:
DS003944,Salisbury2021_First.Modality:
eeg. Subjects: 82; recordings: 82; 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/ds003944 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003944 DOI: https://doi.org/10.18112/openneuro.ds003944.v1.0.1 NEMAR citation count: 7
Examples
>>> from eegdash.dataset import DS003944 >>> dataset = DS003944(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/ds003944").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 ds003944 to reproduce the tutorial on this dataset.
Citation
Dean Salisbury, Dylan Seebold, Brian Coffman (n.d.). EEG: First Episode Psychosis vs. Control Resting Task 1. 10.18112/openneuro.ds003944.v1.0.1
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003944.v1.0.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset