DS004745#

8-Channel SSVEP EEG Dataset with Artifact Trials

Access recordings and metadata through EEGDash.

Citation: Velu Prabhakar Kumaravel, Victor Kartsch, Simone Benatti, Giorgio Vallortigara, Elisabetta Farella, Marco Buiatti (2023). 8-Channel SSVEP EEG Dataset with Artifact Trials. 10.18112/openneuro.ds004745.v1.0.1

Modality: eeg Subjects: 6 Recordings: 30 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004745

dataset = DS004745(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 = DS004745(cache_dir="./data", subject="01")

Advanced query

dataset = DS004745(
    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{ds004745,
  title = {8-Channel SSVEP EEG Dataset with Artifact Trials},
  author = {Velu Prabhakar Kumaravel and Victor Kartsch and Simone Benatti and Giorgio Vallortigara and Elisabetta Farella and Marco Buiatti},
  doi = {10.18112/openneuro.ds004745.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004745.v1.0.1},
}

About This Dataset#

Dataset consists of 6 participants who performed SSVEP tasks. We designed stimulations at 3 different frequencies (2 Hz, 4 Hz, 8 Hz). Each participant attended to 3 trials for each frequency in which they remained static as much as possible to avoid artifacts. They attended to 3 trials for each frequency in which they made voluntary head/neck and eye movements. Please refer to Kumaravel et al., (IEEE EMBC 2021) for further details.

Dataset Information#

Dataset ID	`DS004745`
Title	8-Channel SSVEP EEG Dataset with Artifact Trials
Year	2023
Authors	Velu Prabhakar Kumaravel, Victor Kartsch, Simone Benatti, Giorgio Vallortigara, Elisabetta Farella, Marco Buiatti
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004745.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: 6
Recordings: 30
Tasks: 1

Channels & sampling rate

Channels: 8
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS004745 class to access this dataset programmatically.

class eegdash.dataset.DS004745(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004745. Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 6; recordings: 6; 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/ds004745 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004745

Examples

>>> from eegdash.dataset import DS004745
>>> dataset = DS004745(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