ON006910: ieeg dataset, 121 subjects#
Auditory Naming EC
Access recordings and metadata through EEGDash.
Citation: Ryuzaburo Kochi, Aya Kanno, Hiroshi Uda, Keisuke Hatano, Masaki Sonoda, Hidenori Endo, Michael Cools, Robert Rothermel, Aimee F. Luat, Eishi Asano (2025). Auditory Naming EC. 10.82901/nemar.on006910
Modality: ieeg Subjects: 121 Recordings: 384 License: CC0 Source: nemar
Metadata: Complete (100%)
121-participant iEEG dataset — Auditory Naming EC.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON006910
dataset = ON006910(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON006910(cache_dir="./data", subject="01")
Advanced query
dataset = ON006910(
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{on006910,
title = {Auditory Naming EC},
author = {Ryuzaburo Kochi and Aya Kanno and Hiroshi Uda and Keisuke Hatano and Masaki Sonoda and Hidenori Endo and Michael Cools and Robert Rothermel and Aimee F. Luat and Eishi Asano},
doi = {10.82901/nemar.on006910},
url = {https://doi.org/10.82901/nemar.on006910},
}
About This Dataset#
This dataset, used in the analysis reported by Kochi et al., (2025), contains intracranial EEG recordings from 121 individuals who performed an auditory‑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 402 – stimulus offset 501 – response onset Reference:
Ryuzaburo Kochi, Aya Kanno, Hiroshi Uda, Keisuke Hatano, Masaki Sonoda, Hidenori Endo, Michael Cools, Robert Rothermel, Aimee F. Luat, Eishi Asano. Whole-Brain Millisecond-Scale Effective Connectivity Atlases of Speech
Cohort#
Dataset Statistics#
Age distribution by gender (n=121, range 5–49 yr, mean 14.8 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 1000.0 Hz (n=384 recordings)
Signal · Electrodes & live trace#
Live trace viewer — sub-098 · ses-3 · task-auditory
Showing one representative recording out of
121 subjects and 384 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _ieeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?ieeg=<url>) to inspect it.
Electrode layout — iEEG · 128 sensors — 128 channels
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
Auditory Naming EC |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2025 |
Authors |
Ryuzaburo Kochi, Aya Kanno, Hiroshi Uda, Keisuke Hatano, Masaki Sonoda, Hidenori Endo, Michael Cools, Robert Rothermel, Aimee F. Luat, Eishi Asano |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on006910,
title = {Auditory Naming EC},
author = {Ryuzaburo Kochi and Aya Kanno and Hiroshi Uda and Keisuke Hatano and Masaki Sonoda and Hidenori Endo and Michael Cools and Robert Rothermel and Aimee F. Luat and Eishi Asano},
doi = {10.82901/nemar.on006910},
url = {https://doi.org/10.82901/nemar.on006910},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON006910(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Auditory Naming EC
- Study:
on006910(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON006910.Modality:
ieeg; Subject type:Unknown. Subjects: 121; recordings: 384; 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/on006910 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on006910 DOI: https://doi.org/10.82901/nemar.on006910
Examples
>>> from eegdash.dataset import ON006910 >>> dataset = ON006910(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: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for on006910 to reproduce the tutorial on this dataset.
Citation
Ryuzaburo Kochi, Aya Kanno, Hiroshi Uda, Keisuke Hatano, Masaki Sonoda, … (2025). Auditory Naming EC. 10.82901/nemar.on006910
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on006910.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset