ON006233: ieeg dataset, 108 subjects#
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. 10.82901/nemar.on006233
Modality: ieeg Subjects: 108 Recordings: 347 License: CC0 Source: nemar
Metadata: Complete (100%)
108-participant iEEG dataset — Picture naming.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON006233
dataset = ON006233(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON006233(cache_dir="./data", subject="01")
Advanced query
dataset = ON006233(
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{on006233,
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 = {10.82901/nemar.on006233},
url = {https://doi.org/10.82901/nemar.on006233},
}
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
Cohort#
Dataset Statistics#
Age distribution by gender (n=108, range 5–49 yr, mean 14.8 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 1000.0 Hz (n=347 recordings)
Signal · Electrodes & live trace#
Live trace viewer — sub-098 · ses-3 · task-picture
Showing one representative recording out of
108 subjects and 347 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 · 150 sensors — 150 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 |
Picture naming |
Author (year) |
— |
Canonical |
— |
Importable as |
|
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 |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on006233,
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 = {10.82901/nemar.on006233},
url = {https://doi.org/10.82901/nemar.on006233},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON006233(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Picture naming
- Study:
on006233(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON006233.Modality:
ieeg; Subject type:Unknown. 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
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/on006233 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on006233 DOI: https://doi.org/10.82901/nemar.on006233
Examples
>>> from eegdash.dataset import ON006233 >>> dataset = ON006233(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 on006233 to reproduce the tutorial on this dataset.
Citation
Ryuzaburo Kochi, Aya Kanno, Hiroshi Uda, Keisuke Hatano, Hidenori Endo, … (2025). Picture naming. 10.82901/nemar.on006233
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on006233.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset