DS004977#

CARLA: Adjusted common average referencing for cortico-cortical evoked potential data

Access recordings and metadata through EEGDash.

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

Modality: ieeg Subjects: 4 Recordings: 6 License: CC0 Source: openneuro Citations: 2.0

Metadata: Good (80%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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#

CARLA: Adjusted common average referencing for cortico-cortical evoked potential data

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. 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.

Dataset Information#

Dataset ID	`DS004977`
Title	CARLA: Adjusted common average referencing for cortico-cortical evoked potential data
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	doi:10.18112/openneuro.ds004977.v1.2.0
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: 4
Recordings: 6
Tasks: 1

Channels & sampling rate

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

API Reference#

Use the DS004977 class to access this dataset programmatically.

class eegdash.dataset.DS004977(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004977. Modality: ieeg; Experiment type: Other; Subject type: Epilepsy. Subjects: 5; recordings: 4479; 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/ds004977 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004977

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, 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