DS004977: ieeg dataset, 4 subjects#
CARLA: Adjusted common average referencing for cortico-cortical evoked potential data
Citation: Harvey Huang, Gabriela Ojeda Valencia, Nicholas M Gregg, Gamaleldin M Osman, Morgan N Montoya, Gregory A Worrell, Kai J Miller, Dora Hermes (2024). CARLA: Adjusted common average referencing for cortico-cortical evoked potential data. 10.18112/openneuro.ds004977.v1.2.0
4-participant iEEG dataset — CARLA: Adjusted common average referencing for cortico-cortical evoked potential data.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004977
dataset = DS004977(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004977(cache_dir="./data", subject="01")
Advanced query
dataset = DS004977(
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{ds004977,
title = {CARLA: Adjusted common average referencing for cortico-cortical evoked potential data},
author = {Harvey Huang and Gabriela Ojeda Valencia and Nicholas M Gregg and Gamaleldin M Osman and Morgan N Montoya and Gregory A Worrell and Kai J Miller and Dora Hermes},
doi = {10.18112/openneuro.ds004977.v1.2.0},
url = {https://doi.org/10.18112/openneuro.ds004977.v1.2.0},
}
About This Dataset#
This dataset contains intracranial EEG recordings from four patients during single pulse electrical stimulation as described in:
* H Huang, G Ojeda Valencia, NM Gregg, GM Osman, MN Montoya, GA Worrell, KJ Miller, and D Hermes. (2024). CARLA: Adjusted common average referencing for cortico-cortical evoked potential data. Journal of Neuroscience Methods, 110153. DOI: https://doi.org/10.1016/j.jneumeth.2024.110153.
Currently, this dataset contains the raw data needed to generate all results EXCEPT for those pertaining to figures 7 and 8 (unavailable data samples are censored with 0). The complete data are currently being used to answer other scientific questions, and will be released in time with other manuscripts.
CARLA: Adjusted common average referencing for cortico-cortical evoked potential data
Please cite this work when using the data. These data were recorded at the Mayo Clinic in Rochester, MN, as part of the NIH Brain Initiative supported project R01 MH122258 “CRCNS: Processing speed in the human connectome across the lifespan”. Research reported in this publication was supported by the National Institute Of Mental Health of the National Institutes of Health under Award Number R01MH122258, by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number T32GM145408, and by the American Epilepsy Society under award number 937450. The project was also supported by the Mayo Clinic DERIVE Office and the Mayo Clinic Center for Biomedical Discovery. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The data were collected by Harvey Huang, Dora Hermes, Nicholas M. Gregg, Gamaleldin M. Osman, and Cindy Nelson. The BIDS formatting was performed by Harvey Huang, Dora Hermes, Gabriela Ojeda Valencia, and Morgan Montoya. The iEEG data collection was facilitated by Gregory A. Worrell and Kai J. Miller. Data can be analyzed using the Matlab code at: * hharveygit/CARLA_JNM
Format
Data are formatted according to BIDS version 1.14.0
Single pulse stimulation
The patient were resting in the hospital bed, while single pulse stimulation was performed with a frequency of ~0.2 Hz. The stimulation had a duration of 200 microseconds, was biphasic and had an amplitude of 6mA.
Contact
Please contact Harvey Huang (huang.harvey@mayo.edu) or Dora Hermes (hermes.dora@mayo.edu) for questions.
Cohort#
Dataset Statistics#
Age distribution (n=4, range 16–19 yr, mean 18.0 yr · sex per subject not reported)
Sex composition
Channel counts (ch)
Sampling frequencies: 4800.0 Hz (n=6 recordings)
Total recording duration: 52 min
Signal · Electrodes & live trace#
Electrode layout — iEEG · 228 sensors — 228 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 |
CARLA: Adjusted common average referencing for cortico-cortical evoked potential data |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2024 |
Authors |
Harvey Huang, Gabriela Ojeda Valencia, Nicholas M Gregg, Gamaleldin M Osman, Morgan N Montoya, Gregory A Worrell, Kai J Miller, Dora Hermes |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004977,
title = {CARLA: Adjusted common average referencing for cortico-cortical evoked potential data},
author = {Harvey Huang and Gabriela Ojeda Valencia and Nicholas M Gregg and Gamaleldin M Osman and Morgan N Montoya and Gregory A Worrell and Kai J Miller and Dora Hermes},
doi = {10.18112/openneuro.ds004977.v1.2.0},
url = {https://doi.org/10.18112/openneuro.ds004977.v1.2.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004977 · Huang2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004977(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
CARLA: Adjusted common average referencing for cortico-cortical evoked potential data
- Study:
ds004977(OpenNeuro)- Author (year):
Huang2024- Canonical:
—
Also importable as:
DS004977,Huang2024.Modality:
ieeg; Experiment type:Other; Subject type:Epilepsy. Subjects: 4; recordings: 6; 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/ds004977 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004977 DOI: https://doi.org/10.18112/openneuro.ds004977.v1.2.0 NEMAR citation count: 2
Examples
>>> from eegdash.dataset import DS004977 >>> dataset = DS004977(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/ds004977").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 ds004977 to reproduce the tutorial on this dataset.
Citation
Harvey Huang, Gabriela Ojeda Valencia, Nicholas M Gregg, Gamaleldin M Osman, Morgan N Montoya, … (2024). CARLA: Adjusted common average referencing for cortico-cortical evoked potential data. 10.18112/openneuro.ds004977.v1.2.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004977.v1.2.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset