DS005931#

Visuomotor_task

Access recordings and metadata through EEGDash.

Citation: Riyo Ueda, Eishi Asano (2025). Visuomotor_task. 10.18112/openneuro.ds005931.v1.0.0

Modality: ieeg Subjects: 8 Recordings: 69 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005931

dataset = DS005931(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS005931(cache_dir="./data", subject="01")

Advanced query

dataset = DS005931(
    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{ds005931,
  title = {Visuomotor_task},
  author = {Riyo Ueda and Eishi Asano},
  doi = {10.18112/openneuro.ds005931.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005931.v1.0.0},
}

About This Dataset#

Dataset of intracranial EEG from human epilepsy patients performing a visuomotor task

Description:

We present an electrophysiological dataset recorded from ten subjects during a visuomotor task. Subjects were epilepsy patients undergoing intracranial monitoring for localization of epileptic seizures. Subjects completed five sessions of Speed Match - a visuomotor on the Lumosity platform (https://www.lumosity.com/; Lumos Labs, Inc, San Francisco, CA) - during interictal EEG recording.

Repository structure:

Main directory (interictal EEG from children during gameplay): Contains interictal EEG files of each participant in the study. Folders are explained below.

Subfolders:

sub-/: Contains folders for each subject, named sub-. sub-/ses-: Contains folders for visuomotor task. sub-/ses-/ieeg/: Contains the raw iEEG data in .edf format for each subject. Each subject performed visuomotor task (ses-task). Each *ieeg.edf file contains continuous iEEG data during the visuomotor task. Details about the channels are given in the corresponding .tsv file. We also provide the information on the timing of the finger tapping on ieeg/edf file by specifying the start and end sample of each trial. (101 is for finger tapping).

Dataset Information#

Dataset ID

DS005931

Title

Visuomotor_task

Year

2025

Authors

Riyo Ueda, Eishi Asano

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005931.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005931,
  title = {Visuomotor_task},
  author = {Riyo Ueda and Eishi Asano},
  doi = {10.18112/openneuro.ds005931.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005931.v1.0.0},
}

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: 8

  • Recordings: 69

  • Tasks: 1

Channels & sampling rate

  • Channels: 128 (24), 112 (4), 110 (4)

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Epilepsy

  • Modality: Visual

  • Type: Motor

Files & format

  • Size on disk: 817.7 MB

  • File count: 69

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds005931.v1.0.0

Provenance

API Reference#

Use the DS005931 class to access this dataset programmatically.

class eegdash.dataset.DS005931(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005931. Modality: ieeg; Experiment type: Motor; Subject type: Epilepsy. Subjects: 8; recordings: 16; 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/ds005931 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005931

Examples

>>> from eegdash.dataset import DS005931
>>> dataset = DS005931(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

See Also#