DS004770#

iEEG on children during gameplay

Access recordings and metadata through EEGDash.

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 (2023). iEEG on children during gameplay. 10.18112/openneuro.ds004770.v1.0.0

Modality: ieeg Subjects: 10 Recordings: 93 License: CC0 Source: openneuro Citations: 2.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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:

sub-/: Contains folders for each subject, named sub- and session information.
sub-/ses-: Contains folders for base and task.
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.

Dataset Information#

Dataset ID	`DS004770`
Title	iEEG on children during gameplay
Year	2023
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	doi:10.18112/openneuro.ds004770.v1.0.0
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: 10
Recordings: 93
Tasks: 1

Channels & sampling rate

Channels: 128 (28), 105 (4), 113 (4), 110 (4), 112 (4)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS004770 class to access this dataset programmatically.

class eegdash.dataset.DS004770(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004770. 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

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/ds004770 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004770

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, 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