DS004473: ieeg dataset, 8 subjects#
sEEG Forced Two-Choice Task
Citation: Alexander P. Rockhill, Alessandra Mantovani, Brittany Stedelin, Admed M. Raslan, Nicole C. Swann (2022). sEEG Forced Two-Choice Task. 10.18112/openneuro.ds004473.v1.0.1
8-participant iEEG dataset — sEEG Forced Two-Choice Task.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004473
dataset = DS004473(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004473(cache_dir="./data", subject="01")
Advanced query
dataset = DS004473(
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{ds004473,
title = {sEEG Forced Two-Choice Task},
author = {Alexander P. Rockhill and Alessandra Mantovani and Brittany Stedelin and Admed M. Raslan and Nicole C. Swann},
doi = {10.18112/openneuro.ds004473.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds004473.v1.0.1},
}
About This Dataset#
Welcome to our dataset! Here we present stereoelectroencephalography data from a forced two-choice response task collected in the epilepsy monitoring unit at Oregon Health & Science University. The data was analyzed in collaboration with the University of Oregon. The accompanying paper the first reference below.
Rockhill, A. P., Mantovani, A., Stedelin, B., Nerison, C. S., Raslan, A. M., & Swann, N. C. (2022). Stereo-EEG recordings extend known distributions of canonical movement-related oscillations. Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/acae0a
Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Holdgraf, C., Appelhoff, S., Bickel, S., Bouchard, K., D’Ambrosio, S., David, O., … Hermes, D. (2019). iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology. Scientific Data, 6, 102. https://doi.org/10.1038/s41597-019-0105-7
Cohort#
Dataset Statistics#
Age distribution by gender (n=8, range 26–47 yr, mean 35.1 yr)
Sex composition
Channel counts: 129 ch (n=8 recordings)
Sampling frequencies: 999.4121105232217 Hz (n=8 recordings)
Total recording duration: 6 h 59 min
Signal · Electrodes & live trace#
Live trace viewer — sub-12 · task-SlowFast
Showing one representative recording out of
8 subjects and 8 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 · 121 sensors — 121 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 |
sEEG Forced Two-Choice Task |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2022 |
Authors |
Alexander P. Rockhill, Alessandra Mantovani, Brittany Stedelin, Admed M. Raslan, Nicole C. Swann |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004473,
title = {sEEG Forced Two-Choice Task},
author = {Alexander P. Rockhill and Alessandra Mantovani and Brittany Stedelin and Admed M. Raslan and Nicole C. Swann},
doi = {10.18112/openneuro.ds004473.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds004473.v1.0.1},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004473 · Rockhill2023eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004473(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
sEEG Forced Two-Choice Task
- Study:
ds004473(OpenNeuro)- Author (year):
Rockhill2023- Canonical:
—
Also importable as:
DS004473,Rockhill2023.Modality:
ieeg; Experiment type:Motor; Subject type:Epilepsy. Subjects: 8; recordings: 8; 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/ds004473 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004473 DOI: https://doi.org/10.18112/openneuro.ds004473.v1.0.1 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS004473 >>> dataset = DS004473(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/ds004473").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 ds004473 to reproduce the tutorial on this dataset.
Citation
Alexander P. Rockhill, Alessandra Mantovani, Brittany Stedelin, Admed M. Raslan, Nicole C. Swann (2022). sEEG Forced Two-Choice Task. 10.18112/openneuro.ds004473.v1.0.1
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004473.v1.0.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset