DS005545#

Auditory naming

Access recordings and metadata through EEGDash.

Citation: Aya Kanno, Ryuzaburo Kochi, Kazuki Sakakura, Yu Kitazawa, Hiroshi Uda, Riyo Ueda, Masaki Sonoda, Min-Hee Lee, Jeong-Won Jeong, Aimee F. Luat, Eishi Asano (2024). Auditory naming. 10.18112/openneuro.ds005545.v1.0.3

Modality: ieeg Subjects: 106 Recordings: 1349 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005545

dataset = DS005545(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 = DS005545(cache_dir="./data", subject="01")

Advanced query

dataset = DS005545(
    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{ds005545,
  title = {Auditory naming},
  author = {Aya Kanno and Ryuzaburo Kochi and Kazuki Sakakura and Yu Kitazawa and Hiroshi Uda and Riyo Ueda and Masaki Sonoda and Min-Hee Lee and Jeong-Won Jeong and Aimee F. Luat and Eishi Asano},
  doi = {10.18112/openneuro.ds005545.v1.0.3},
  url = {https://doi.org/10.18112/openneuro.ds005545.v1.0.3},
}

About This Dataset#

This dataset, used in the analysis reported by Kanno et al., (2025), contains intracranial EEG recordings from 106 individuals who performed an auditory‑naming task. The corresponding MATLAB analysis code is available at a8k8nn0/TractographyAtlas, and 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 402 – stimulus offset 501 – response onset

Reference: Aya Kanno, Ryuzaburo Kochi, Kazuki Sakakura, Yu Kitazawa, Hiroshi Uda, Riyo Ueda, Masaki Sonoda, Min-Hee Lee, Jeong-Won Jeong, Robert Rothermel, Aimee F. Luat, Eishi Asano. Dynamic Causal Tractography Analysis of Auditory Descriptive Naming: An Intracranial Study of 106 Patients. bioRxiv 2025.03.07.641428; doi: https://doi.org/10.1101/2025.03.07.641428

Dataset Information#

Dataset ID	`DS005545`
Title	Auditory naming
Year	2024
Authors	Aya Kanno, Ryuzaburo Kochi, Kazuki Sakakura, Yu Kitazawa, Hiroshi Uda, Riyo Ueda, Masaki Sonoda, Min-Hee Lee, Jeong-Won Jeong, Aimee F. Luat, Eishi Asano
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005545.v1.0.3
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: 106
Recordings: 1349
Tasks: 1

Channels & sampling rate

Channels: 128 (474), 138 (28), 134 (22), 136 (22), 140 (16), 112 (12), 110 (12), 156 (10), 150 (10), 142 (10), 132 (8), 164 (8), 148 (8), 144 (8), 118 (6), 96 (6), 84 (6), 116 (6)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS005545 class to access this dataset programmatically.

class eegdash.dataset.DS005545(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005545. Modality: ieeg; Experiment type: Memory; Subject type: Unknown. Subjects: 106; recordings: 336; 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/ds005545 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005545

Examples

>>> from eegdash.dataset import DS005545
>>> dataset = DS005545(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