DS004368#

Meta-rdk: Preprocessed EEG data

Access recordings and metadata through EEGDash.

Citation: Martin Rouy, Matthieu Roger, Dorian Goueytes, Michael Pereira, Paul Roux, Nathan Faivre (2022). Meta-rdk: Preprocessed EEG data. 10.18112/openneuro.ds004368.v1.0.2

Modality: eeg Subjects: 39 Recordings: 206 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004368

dataset = DS004368(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 = DS004368(cache_dir="./data", subject="01")

Advanced query

dataset = DS004368(
    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{ds004368,
  title = {Meta-rdk: Preprocessed EEG data},
  author = {Martin Rouy and Matthieu Roger and Dorian Goueytes and Michael Pereira and Paul Roux and Nathan Faivre},
  doi = {10.18112/openneuro.ds004368.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004368.v1.0.2},
}

About This Dataset#

The study was approved by the ethical committee Sud Méditérannée II (217 R01). Twenty individuals with a schizophrenia spectrum disorder (schizophrenia or schizoaffective disorder, 16 males, 4 females) and 22 healthy participants (15 males, 7 females) from the general population took part in this study. Schizophrenia and schizoaffective disorders were diagnosed based on the Structured Clinical Interview for assessing the DSM-5 criteria. The control group was screened for current or past psychiatric illness, and individuals were excluded if they met the criteria for a severe and persistent mental disorder. We used a visual discrimination task. Stimuli consisted of 100 moving dots within a circle (3° radius) at the center of the screen. On each trial, participants indicated whether the motion direction of the dots was to the left or to the right by reaching and clicking on one of two choice targets (3° radius circle) at the top corners of the screen with a mouse. After 6 seconds without response, a buzz sound rang and a message was displayed inviting the participant to respond quicker. Motion coherence was adapted at the individual level via a 1up/2down staircase procedure in order to match task-performance between groups. Following each perceptual decision, participants were asked to report their confidence about their response using a vertical visual analog scale from 0% (Sure incorrect) to 100% (Sure correct), with 50% confidence meaning “Not sure at all”.

Dataset Information#

Dataset ID	`DS004368`
Title	Meta-rdk: Preprocessed EEG data
Year	2022
Authors	Martin Rouy, Matthieu Roger, Dorian Goueytes, Michael Pereira, Paul Roux, Nathan Faivre
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004368.v1.0.2
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: 39
Recordings: 206
Tasks: 1

Channels & sampling rate

Channels: 63
Sampling rate (Hz): 128.0
Duration (hours): 0.0

API Reference#

Use the DS004368 class to access this dataset programmatically.

class eegdash.dataset.DS004368(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004368. Modality: eeg; Experiment type: Perception; Subject type: Schizophrenia/Psychosis. Subjects: 39; recordings: 40; 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/ds004368 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004368

Examples

>>> from eegdash.dataset import DS004368
>>> dataset = DS004368(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