DS003420#

HD-EEGtask(Dataset 1)

Access recordings and metadata through EEGDash.

Citation: Ahmad Mheich, Olivier Dufor, Sahar Yassine, Aya Kabbara, Arnaud Biraben, Fabrice Wendling, Mahmoud Hassan (2020). HD-EEGtask(Dataset 1). 10.18112/openneuro.ds003420.v1.0.2

Modality: eeg Subjects: 23 Recordings: 92 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003420

dataset = DS003420(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 = DS003420(cache_dir="./data", subject="01")

Advanced query

dataset = DS003420(
    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{ds003420,
  title = {HD-EEGtask(Dataset 1)},
  author = {Ahmad Mheich and Olivier Dufor and Sahar Yassine and Aya Kabbara and Arnaud Biraben and Fabrice Wendling and Mahmoud Hassan},
  doi = {10.18112/openneuro.ds003420.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds003420.v1.0.2},
}

About This Dataset#

Dataset 1

Presentation

This dataset was collected between 2012 and 2013 in Rennes (France) during two conditions (visual naming and spelling tasks). The dataset consists of naming and spelling the names of visually presented objects. The data was collected in the Rennes University Hospital. This experiment was approved by an independent ethics committee and authorized by the French institutional review board (IRB): “Comite de Protection des Personnes dans la Recherche Biomedicale Ouest V” (CCPPRB-Ouest V). This study was registered under the name “conneXion” and the agreement number: 2012- A01227-36.

Dataset Information#

Dataset ID	`DS003420`
Title	HD-EEGtask(Dataset 1)
Year	2020
Authors	Ahmad Mheich, Olivier Dufor, Sahar Yassine, Aya Kabbara, Arnaud Biraben, Fabrice Wendling, Mahmoud Hassan
License	CC0
Citation / DOI	10.18112/openneuro.ds003420.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: 23
Recordings: 92
Tasks: —

Channels & sampling rate

Channels: 256 (160), 257 (24)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS003420 class to access this dataset programmatically.

class eegdash.dataset.DS003420(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003420. Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 23; recordings: 92; tasks: 0.

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/ds003420 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003420

Examples

>>> from eegdash.dataset import DS003420
>>> dataset = DS003420(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