DS004696: ieeg dataset, 8 subjects#
HAPwave_bids
Citation: Ojeda Valencia, G., Gregg, N., Huang, H., Lundstrom, B., Brinkmann, B., Pal Attia1, T., Van Gompel, J., Bernstein,M., In, M., Huston, J., Worrell1, G., Miller, K., Hermes, D. (20). HAPwave_bids. 10.18112/openneuro.ds004696.v1.0.1
8-participant iEEG dataset — HAPwave_bids.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004696
dataset = DS004696(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004696(cache_dir="./data", subject="01")
Advanced query
dataset = DS004696(
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{ds004696,
title = {HAPwave_bids},
author = {Ojeda Valencia, G. and Gregg, N. and Huang, H. and Lundstrom, B. and Brinkmann, B. and Pal Attia1, T. and Van Gompel, J. and Bernstein,M. and In, M. and Huston, J. and Worrell1, G. and Miller, K. and Hermes, D.},
doi = {10.18112/openneuro.ds004696.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds004696.v1.0.1},
}
About This Dataset#
This dataset contains intracranial EEG (iEEG) recordings from 8 patients during single pulse electrical stimulation used in the publication of:
Ojeda Valencia G, Gregg N, Huang H, Lundstrom B, Brinkmann B, Pal Attia T, Van Gompel J, Bernstein M, In MH, Huston J, Worrell G, Miller KJ, and Hermes D. 2023. Signatures of electrical stimulation driven network interactions in the human limbic system. Journal of Neuroscience (in press).
Information
License
This dataset is made available under the Public Domain Dedication and License CC v1.0, whose full text can be found at https://creativecommons.org/publicdomain/zero/1.0/.
We hope that all users will follow the ODC Attribution/Share-Alike Community Norms (http://www.opendatacommons.org/norms/odc-by-sa/); in particular, while not legally required, we hope that all users of the data will acknowledge by citing the following in any publication:
View full README
Information
License
This dataset is made available under the Public Domain Dedication and License CC v1.0, whose full text can be found at https://creativecommons.org/publicdomain/zero/1.0/.
We hope that all users will follow the ODC Attribution/Share-Alike Community Norms (http://www.opendatacommons.org/norms/odc-by-sa/); in particular, while not legally required, we hope that all users of the data will acknowledge by citing the following in any publication:
Ojeda Valencia G, Gregg N, Huang H, Lundstrom B, Brinkmann B, Pal Attia T, Van Gompel J, Bernstein M, In MH, Huston J, Worrell G, Miller KJ, and Hermes D. 2023. Signatures of electrical stimulation driven network interactions in the human limbic system. Journal of Neuroscience. DOI: https://doi.org/10.1523/JNEUROSCI.2201-22.2023
Task Description
Patients were resting in the hospital bed, while single pulse stimulation was performed. The stimulation had a duration of 200 microseconds, was biphasic and had an amplitude of 6mA. For subject 7 stimulation amplitude was sometimes reduced to 4mA to minimize interictal responses.
Code
Code to analyses these data is available at: MultimodalNeuroimagingLab/HAPwave
Dataset
This data is organized according to the Brain Imaging Data Structure specification (BIDS version 1.12.0). A community- driven specification for organizing neurophysiology data along with its metadata. For more information on this data specification, see https://bids-specification.readthedocs.io/en/stable/ Each subject has their own folder (e.g., ‘sub-01’) containing intracranial EEG (iEEG) recordings from 8 patients during single pulse electrical stimulation, as well as the metadata needed to understand the raw data and event timing.
Acknowledgements
This project was funded by the National Institute Of Mental Health of the National Institutes of Health Brain Initiative under Award Number R01 MH122258, “CRCNS: Processing speed in the human connectome across the lifespan”. The overall goal of this project is to develop a large database of single pulse stimulation data and develop tools to advance our understanding of the human connectome across the lifespan. The data was collected by Dora Hermes, Nick Gregg, Brian Lundstrom, Cindy Nelson, Gabriela Ojeda Valencia, Gregg Worrell and Kai J. Miller. The BIDS formatting was performed by Dora Hermes and Gabriela Ojeda Valencia.
Contact
Please contact Dora Hermes (hermes.dora@mayo.edu) or Gabriela Ojeda Valencia (OjedaValencia.Alma@mayo.edu) for questions.
Cohort#
Dataset Statistics#
Age distribution (n=8, range 13–63 yr, mean 30.1 yr · sex per subject not reported)
Sex composition
Channel counts (ch)
Sampling frequencies: 2048.0 Hz (n=8 recordings)
Total recording duration: 9 h 7 min
Signal · Electrodes & live trace#
Electrode layout — iEEG · 211 sensors — 211 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 |
HAPwave_bids |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Ojeda Valencia, G., Gregg, N., Huang, H., Lundstrom, B., Brinkmann, B., Pal Attia1, T., Van Gompel, J., Bernstein,M., In, M., Huston, J., Worrell1, G., Miller, K., Hermes, D. |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004696,
title = {HAPwave_bids},
author = {Ojeda Valencia, G. and Gregg, N. and Huang, H. and Lundstrom, B. and Brinkmann, B. and Pal Attia1, T. and Van Gompel, J. and Bernstein,M. and In, M. and Huston, J. and Worrell1, G. and Miller, K. and Hermes, D.},
doi = {10.18112/openneuro.ds004696.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds004696.v1.0.1},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004696 · Valencia2023eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004696(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
HAPwave_bids
- Study:
ds004696(OpenNeuro)- Author (year):
Valencia2023- Canonical:
—
Also importable as:
DS004696,Valencia2023.Modality:
ieeg; Experiment type:Clinical/Intervention; 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/ds004696 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004696 DOI: https://doi.org/10.18112/openneuro.ds004696.v1.0.1 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS004696 >>> dataset = DS004696(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/ds004696").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 ds004696 to reproduce the tutorial on this dataset.
Citation
Ojeda Valencia, G., Gregg, N., Huang, H., Lundstrom, B., Brinkmann, B., … (20). HAPwave_bids. 10.18112/openneuro.ds004696.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.ds004696.v1.0.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset