EEGdash›OpenNeuro›DS003708
Iss. 3708 · 1 subjects · 1 recordings · CC0
Dataset Brief · Basis profile curve identification to understand electrical s…

DS003708: ieeg dataset, 1 subjects#

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

Citation: Dora Hermes, Gabriella Ojeda, Kai J. Miller, Multimodal Neuroimaging Laboratory at Mayo Clinic (20). Basis profile curve identification to understand electrical stimulation effects in human brain networks. 10.18112/openneuro.ds003708.v1.0.0

1-participant iEEG dataset — Basis profile curve identification to understand electrical stimulation effects in human brain networks.

Data & curation Dora Hermes · Gabriella Ojeda · Kai J. Miller · Multimodal Neuroimaging Laboratory at Mayo Clinic
Year 20 · Distributed via OpenNeuro
Funding R01 MH122258 CRCNS: Processing speed in the human connectome across the lifespan
iEEG · 89 ch2048 HzBIDS v 1.3.0Task · ccepOtherClinical/Intervention
Layer 01Study
What was asked
Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.
Layer 02Signal · BIDS
What was recorded
Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.
Layer 03Training · ML
What you can train on
Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.
§ 01Access · Get started

Quickstart#

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)

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'])

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},
}
§ 02Study · The README

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.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 89 ch (n=1 recordings)

Sampling frequencies: 2048.0 Hz (n=1 recordings)

Total recording duration: 1 h 6 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 89 ch · iEEG · 2048 Hz · 1 subjects, 1 recordings
Electrode layout — iEEG · 84 sensors — 84 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.

NEMAR pipeline statistics — DS003708
HED event descriptors word cloud HED event descriptors word cloud — DS003708
§ 05Manifest · BIDS tree

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.

Recordings—
Files—
Subjects—
Modalities—
Click to load file structure…
Full dataset metadata table

Dataset ID

DS003708

Title

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

Author (year)

Hermes2021

Canonical

—

Importable as

DS003708, Hermes2021

Year

20

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
Copy-paste 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},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS003708(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Hermes2021
Canonical—
Importable asDS003708 · Hermes2021
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS003708(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

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

Study:

ds003708 (OpenNeuro)

Author (year):

Hermes2021

Canonical:

—

Also importable as: DS003708, Hermes2021.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Unknown. Subjects: 1; recordings: 1; 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 DOI: https://doi.org/10.18112/openneuro.ds003708.v1.0.0 NEMAR citation count: 1

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: 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.

Access modesMNE → braindecode → PyTorch → ML
.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne
BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode
DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr
Hugging FacePre-bundled mirror at EEGDash/ds003708 · pull with datasets.load_dataset("EEGDash/ds003708").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS003708.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons
Examples using EEGDashcurated · start here
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 ds003708 to reproduce the tutorial on this dataset.

Citation

Dora Hermes, Gabriella Ojeda, Kai J. Miller, Multimodal Neuroimaging Laboratory at Mayo Clinic (20). Basis profile curve identification to understand electrical stimulation effects in human brain networks. 10.18112/openneuro.ds003708.v1.0.0

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds003708.v1.0.0.

BIDS
BIDS v 1.3.0
Sidecars
not yet probed
Machine-readable
schema.org/Dataset · Croissant

See Also#