DS006233#

Picture naming

Access recordings and metadata through EEGDash.

Citation: Ryuzaburo Kochi, Aya Kanno, Hiroshi Uda, Keisuke Hatano, Hidenori Endo, Michael Cools, Robert Rothermel, Aimee F. Luat, Eishi Asano (2025). Picture naming. 10.18112/openneuro.ds006233.v1.0.0

Modality: ieeg Subjects: 108 Recordings: 1393 License: CC0 Source: openneuro

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006233

dataset = DS006233(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS006233(cache_dir="./data", subject="01")

Advanced query

dataset = DS006233(
    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{ds006233,
  title = {Picture naming},
  author = {Ryuzaburo Kochi and Aya Kanno and Hiroshi Uda and Keisuke Hatano and Hidenori Endo and Michael Cools and Robert Rothermel and Aimee F. Luat and Eishi Asano},
  doi = {10.18112/openneuro.ds006233.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006233.v1.0.0},
}

About This Dataset#

This dataset, used in the analysis reported by Kochi et al., (2025), contains intracranial EEG recordings from 108 individuals who performed an picture‑naming task. Electrode coordinates are provided in MNI‑305 space.

Each EDF file is tagged for the auditory naming task with the following event codes: 401 – stimulus onset 501 – response onset

Reference: Ryuzaburo Kochi, Aya Kanno, Hiroshi Uda, Keisuke Hatano, Hidenori Endo, Michael Cools, Robert Rothermel, Aimee F. Luat, Eishi Asano. Naming is Shaped by Early Facilitative and Late Compensatory Neural Interactions: An Intracranial Study of 125 Patients

Dataset Information#

Dataset ID

DS006233

Title

Picture naming

Year

2025

Authors

Ryuzaburo Kochi, Aya Kanno, Hiroshi Uda, Keisuke Hatano, Hidenori Endo, Michael Cools, Robert Rothermel, Aimee F. Luat, Eishi Asano

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006233.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds006233,
  title = {Picture naming},
  author = {Ryuzaburo Kochi and Aya Kanno and Hiroshi Uda and Keisuke Hatano and Hidenori Endo and Michael Cools and Robert Rothermel and Aimee F. Luat and Eishi Asano},
  doi = {10.18112/openneuro.ds006233.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006233.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: 108

  • Recordings: 1393

  • Tasks: 1

Channels & sampling rate

  • Channels: Varies

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Surgery

  • Modality: Visual

  • Type: Other

Files & format

  • Size on disk: 17.3 GB

  • File count: 1393

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds006233.v1.0.0

Provenance

API Reference#

Use the DS006233 class to access this dataset programmatically.

class eegdash.dataset.DS006233(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006233. Modality: ieeg; Experiment type: Other; Subject type: Surgery. Subjects: 108; recordings: 347; 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/ds006233 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006233

Examples

>>> from eegdash.dataset import DS006233
>>> dataset = DS006233(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#