ON008610: ieeg dataset, 5 subjects#
FUS and Tactile Neuromodulation in NHP VPL - Electrophysiology
Access recordings and metadata through EEGDash.
Citation: Ning Zheng, Pai-Feng Yang, M. Anthony Phipps, Jiro Kusunose, Arabinda Mishra, Jixin Xia, William Rodriguez, Allen T. Newton, Benoit M. Dawant, John C. Gore, Charles F. Caskey, Li Min Chen (—). FUS and Tactile Neuromodulation in NHP VPL - Electrophysiology. 10.82901/nemar.on008610
Modality: ieeg Subjects: 5 Recordings: 359 License: CC0 Source: nemar
Metadata: Good (70%)
5-participant iEEG dataset — FUS and Tactile Neuromodulation in NHP VPL - Electrophysiology.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON008610
dataset = ON008610(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON008610(cache_dir="./data", subject="01")
Advanced query
dataset = ON008610(
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{on008610,
title = {FUS and Tactile Neuromodulation in NHP VPL - Electrophysiology},
author = {Ning Zheng and Pai-Feng Yang and M. Anthony Phipps and Jiro Kusunose and Arabinda Mishra and Jixin Xia and William Rodriguez and Allen T. Newton and Benoit M. Dawant and John C. Gore and Charles F. Caskey and Li Min Chen},
doi = {10.82901/nemar.on008610},
url = {https://doi.org/10.82901/nemar.on008610},
}
About This Dataset#
No README content is available for this dataset.
Cohort#
Dataset Statistics#
Age distribution (n=2, range 15–19 yr, mean 17.0 yr · sex per subject not reported)
Signal · Electrodes & live trace#
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
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 |
FUS and Tactile Neuromodulation in NHP VPL - Electrophysiology |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Ning Zheng, Pai-Feng Yang, M. Anthony Phipps, Jiro Kusunose, Arabinda Mishra, Jixin Xia, William Rodriguez, Allen T. Newton, Benoit M. Dawant, John C. Gore, Charles F. Caskey, Li Min Chen |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on008610,
title = {FUS and Tactile Neuromodulation in NHP VPL - Electrophysiology},
author = {Ning Zheng and Pai-Feng Yang and M. Anthony Phipps and Jiro Kusunose and Arabinda Mishra and Jixin Xia and William Rodriguez and Allen T. Newton and Benoit M. Dawant and John C. Gore and Charles F. Caskey and Li Min Chen},
doi = {10.82901/nemar.on008610},
url = {https://doi.org/10.82901/nemar.on008610},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON008610(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
FUS and Tactile Neuromodulation in NHP VPL - Electrophysiology
- Study:
on008610(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON008610.Modality:
ieeg; Subject type:Unknown. Subjects: 5; recordings: 359; tasks: 2.- 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/on008610 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on008610 DOI: https://doi.org/10.82901/nemar.on008610
Examples
>>> from eegdash.dataset import ON008610 >>> dataset = ON008610(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.pytorch
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 on008610 to reproduce the tutorial on this dataset.
Citation
Ning Zheng, Pai-Feng Yang, M. Anthony Phipps, Jiro Kusunose, Arabinda Mishra, … (n.d.). FUS and Tactile Neuromodulation in NHP VPL - Electrophysiology. 10.82901/nemar.on008610
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on008610.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset