DS004457: ieeg dataset, 5 subjects#
Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex
Citation: Harvey Huang, Nicholas M Gregg, Gabriela Ojeda Valencia, Benjamin H Brinkmann, Brian N Lundstrom, Gregory A Worrell, Kai J Miller, Dora Hermes (2022). Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex. 10.18112/openneuro.ds004457.v1.0.1
5-participant iEEG dataset — Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004457
dataset = DS004457(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004457(cache_dir="./data", subject="01")
Advanced query
dataset = DS004457(
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{ds004457,
title = {Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex},
author = {Harvey Huang and Nicholas M Gregg and Gabriela Ojeda Valencia and Benjamin H Brinkmann and Brian N Lundstrom and Gregory A Worrell and Kai J Miller and Dora Hermes},
doi = {10.18112/openneuro.ds004457.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds004457.v1.0.1},
}
About This Dataset#
This dataset contains intracranial EEG recordings from five patients during single pulse electrical stimulation as described in:
* H Huang, NM Gregg, G Ojeda Valencia, BH Brinkmann, BN Lundstrom, GA Worrell, KJ Miller, and D Hermes (2022) Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex. (Under Review)
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 and by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number T32GM065841. 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 was collected by Harvey Huang, Dora Hermes, Nick Gregg, Brian Lundstrom, Cindy Nelson, Gregg Worrell and Kai J. Miller. The BIDS formatting was performed by Harvey Huang, Dora Hermes and Gabriela Ojeda Valencia.
Basis Profile Curve identification in the human ventral temporal cortex
Data can be analyzed using the Matlab code at: * hharveygit/VTCBPC_JNS_Manu
Format
Data are formatted according to BIDS version 1.9.9
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 Dora Hermes (hermes.dora@mayo.edu) for questions.
Cohort#
Dataset Statistics#
Age distribution (n=5, range 13–46 yr, mean 25.4 yr · sex per subject not reported)
Sex composition
Channel counts (ch)
Sampling frequencies: 2048.0 Hz (n=5 recordings)
Total recording duration: 5 h 37 min
Signal · Electrodes & live trace#
Electrode layout — iEEG · 165 sensors — 165 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 |
Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2022 |
Authors |
Harvey Huang, Nicholas M Gregg, Gabriela Ojeda Valencia, Benjamin H Brinkmann, Brian N Lundstrom, Gregory A Worrell, Kai J Miller, Dora Hermes |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004457,
title = {Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex},
author = {Harvey Huang and Nicholas M Gregg and Gabriela Ojeda Valencia and Benjamin H Brinkmann and Brian N Lundstrom and Gregory A Worrell and Kai J Miller and Dora Hermes},
doi = {10.18112/openneuro.ds004457.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds004457.v1.0.1},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004457 · Huang2023eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004457(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex
- Study:
ds004457(OpenNeuro)- Author (year):
Huang2023- Canonical:
—
Also importable as:
DS004457,Huang2023.Modality:
ieeg; Experiment type:Clinical/Intervention; Subject type:Surgery. Subjects: 5; recordings: 5; 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/ds004457 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004457 DOI: https://doi.org/10.18112/openneuro.ds004457.v1.0.1 NEMAR citation count: 3
Examples
>>> from eegdash.dataset import DS004457 >>> dataset = DS004457(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/ds004457").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 ds004457 to reproduce the tutorial on this dataset.
Citation
Harvey Huang, Nicholas M Gregg, Gabriela Ojeda Valencia, Benjamin H Brinkmann, Brian N Lundstrom, … (2022). Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex. 10.18112/openneuro.ds004457.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.ds004457.v1.0.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset