DS004770: ieeg dataset, 10 subjects#
iEEG on children during gameplay
Citation: Riyo Ueda, Kazuki Sakakura, Takumi Mitsuhashi, Masaki Sonoda, Ethan Firestone, Naoto Kuroda, Yu Kitazawa, Hiroshi Uda, Aimee F. Luat, Elizabeth L. Johnson, Noa Ofen, Eishi Asano (—). iEEG on children during gameplay. 10.18112/openneuro.ds004770.v1.0.0
10-participant iEEG dataset — iEEG on children during gameplay.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004770
dataset = DS004770(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004770(cache_dir="./data", subject="01")
Advanced query
dataset = DS004770(
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{ds004770,
title = {iEEG on children during gameplay},
author = {Riyo Ueda and Kazuki Sakakura and Takumi Mitsuhashi and Masaki Sonoda and Ethan Firestone and Naoto Kuroda and Yu Kitazawa and Hiroshi Uda and Aimee F. Luat and Elizabeth L. Johnson and Noa Ofen and Eishi Asano},
doi = {10.18112/openneuro.ds004770.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004770.v1.0.0},
}
About This Dataset#
Dataset of intracranial EEG from human epilepsy patients performing a visuospatial working memory task
Description:
We present an electrophysiological dataset recorded from ten subjects during a visuospatial working memory task. Subjects were epilepsy patients undergoing intracranial monitoring for localization of epileptic seizures. Subjects completed 60 trials (five sessions) of Memory Matrix - a visuospatial working memory game on the Lumosity platform (https://www.lumosity.com/; Lumos Labs, Inc, San Francisco, CA) - during interictal iEEG recording.
Repository structure:
Main directory (iEEG from children during gameplay) Contains iEEG files of each participant in the study. Folders are explained below.
Subfolders: 1. sub-/: Contains folders for each subject, named sub- and session information. 2. sub-/ses-: Contains folders for base and task. 3. sub-/ses-/ieeg/: Contains the raw iEEG data in .edf format for each subject. Each subject performed 60 working memory trials (ses-task). Each *ieeg.edf file contains continuous iEEG data during the working memory task. Details about the channels are given in the corresponding .tsv file. We also provide the information on the timing of the stimulus onset and finger tapping on ieeg/edf file by specifying the start and end sample of each trial. (101 is for task display, 401 is for finger tapping to the successful grid, and 501 is for finger tapping to the failed grid). Each subject also had baseline periods (ses-base). To establish baseline, we selected 60 non-overlapping 2,000-ms time windows during periods of spontaneous, resting, eye-open wakefulness immediately preceding the game sessions.
Cohort#
Dataset Statistics#
Age distribution by gender (n=10, range 9–20 yr, mean 15.0 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 1000.0 Hz (n=22 recordings)
Signal · Electrodes & live trace#
Live trace viewer — sub-08 · ses-base · task-game · run-01
Showing one representative recording out of
10 subjects and 22 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 · 75 sensors — 75 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 |
iEEG on children during gameplay |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Riyo Ueda, Kazuki Sakakura, Takumi Mitsuhashi, Masaki Sonoda, Ethan Firestone, Naoto Kuroda, Yu Kitazawa, Hiroshi Uda, Aimee F. Luat, Elizabeth L. Johnson, Noa Ofen, Eishi Asano |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004770,
title = {iEEG on children during gameplay},
author = {Riyo Ueda and Kazuki Sakakura and Takumi Mitsuhashi and Masaki Sonoda and Ethan Firestone and Naoto Kuroda and Yu Kitazawa and Hiroshi Uda and Aimee F. Luat and Elizabeth L. Johnson and Noa Ofen and Eishi Asano},
doi = {10.18112/openneuro.ds004770.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004770.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004770 · Ueda2023eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004770(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
iEEG on children during gameplay
- Study:
ds004770(OpenNeuro)- Author (year):
Ueda2023- Canonical:
—
Also importable as:
DS004770,Ueda2023.Modality:
ieeg; Experiment type:Memory; Subject type:Epilepsy. Subjects: 10; recordings: 22; 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/ds004770 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004770 DOI: https://doi.org/10.18112/openneuro.ds004770.v1.0.0 NEMAR citation count: 2
Examples
>>> from eegdash.dataset import DS004770 >>> dataset = DS004770(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.pytorchdatasets.load_dataset("EEGDash/ds004770").huggingface
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 ds004770 to reproduce the tutorial on this dataset.
Citation
Riyo Ueda, Kazuki Sakakura, Takumi Mitsuhashi, Masaki Sonoda, Ethan Firestone, … (n.d.). iEEG on children during gameplay. 10.18112/openneuro.ds004770.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004770.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset