DS003944: eeg dataset, 82 subjects#

EEG: First Episode Psychosis vs. Control Resting Task 1

Access recordings and metadata through EEGDash.

Citation: Dean Salisbury, Dylan Seebold, Brian Coffman (2021). EEG: First Episode Psychosis vs. Control Resting Task 1. 10.18112/openneuro.ds003944.v1.0.1

Modality: eeg Subjects: 82 Recordings: 82 License: CC0 Source: openneuro Citations: 7.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.

Dataset Information#

Dataset ID	`DS003944`
Title	EEG: First Episode Psychosis vs. Control Resting Task 1
Author (year)	`Salisbury2021_First`
Canonical	—
Importable as	`DS003944`, `Salisbury2021_First`
Year	2021
Authors	Dean Salisbury, Dylan Seebold, Brian Coffman
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003944.v1.0.1
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: 82
Recordings: 82
Tasks: 1

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 1000.0 (81), 3000.00030000003
Duration (hours): 6.999305547125

Electrode Layout#

Electrode layout — EEG · 61 sensors — 61 channels

Dataset Statistics#

Age distribution (n=82, range 12–35 yr)

101520253035

Sex distribution

28

54

Female Male Total: 82

Channel counts: 64 ch (n=82 recordings)

Sampling frequencies (Hz)

10003000.0

Total recording duration: 6 h 59 min

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS003944 class to access this dataset programmatically.

class eegdash.dataset.DS003944(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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

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/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)