DS003708#

Basis profile curve identification to understand electrical stimulation effects in human brain networks

Access recordings and metadata through EEGDash.

Citation: Dora Hermes, Gabriella Ojeda, Kai J. Miller, Multimodal Neuroimaging Laboratory at Mayo Clinic (2021). Basis profile curve identification to understand electrical stimulation effects in human brain networks. 10.18112/openneuro.ds003708.v1.0.0

Modality: ieeg Subjects: 1 Recordings: 1 License: CC0 Source: openneuro Citations: 1.0

Metadata: Good (80%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003708

dataset = DS003708(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = DS003708(cache_dir="./data", subject="01")

Advanced query

dataset = DS003708(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds003708,
  title = {Basis profile curve identification to understand electrical stimulation effects in human brain networks},
  author = {Dora Hermes and Gabriella Ojeda and Kai J. Miller and Multimodal Neuroimaging Laboratory at Mayo Clinic},
  doi = {10.18112/openneuro.ds003708.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds003708.v1.0.0},
}

About This Dataset#

This dataset contains intracranial EEG recordings from one patient during single pulse electrical stimulation. 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”. The overarching 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.

Citing this dataset

This dataset is part of the paper on ‘Basis profile curve identification to understand electrical stimulation effects in human brain networks’ by Miller, Mueller and Hermes, 2021, https://www.biorxiv.org/content/10.1101/2021.01.24.428020v1.full. This project was funded by the National Institute Of Mental Health of the National Institutes of Health under Award Number R01MH122258 to Dora Hermes (Mayo Clinic). The data was collected by Dora Hermes, Nick Gregg, Brian Lundstrom, Cindy Nelson, Gregg Worrell and Kai J. Miller. The BIDS formatting was performed by Dora Hermes and Gabriella Ojeda Valencia.

Format

It is formatted according to BIDS version 1.3.0

Details about the single pulse stimulation experiment

Patients 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. On the motor cortex stimulation amplitude was sometimes reduced to 1 or 2mA to minimize movement artifacts.

Contact

Please contact Dora Hermes (hermes.dora@mayo.edu) for questions.

Dataset Information#

Dataset ID	`DS003708`
Title	Basis profile curve identification to understand electrical stimulation effects in human brain networks
Year	2021
Authors	Dora Hermes, Gabriella Ojeda, Kai J. Miller, Multimodal Neuroimaging Laboratory at Mayo Clinic
License	CC0
Citation / DOI	10.18112/openneuro.ds003708.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 1
Recordings: 1
Tasks: 1

Channels & sampling rate

Channels: Varies
Sampling rate (Hz): Varies
Duration (hours): 0.0

API Reference#

Use the DS003708 class to access this dataset programmatically.

class eegdash.dataset.DS003708(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003708. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Unknown. Subjects: 2; recordings: 281; 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

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003708 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003708

Examples

>>> from eegdash.dataset import DS003708
>>> dataset = DS003708(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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None