DS005953#

iEEG_visual

Access recordings and metadata through EEGDash.

Citation: Jonathan Winawer, Dora Hermes (2025). iEEG_visual. 10.18112/openneuro.ds005953.v1.0.0

Modality: ieeg Subjects: 2 Recordings: 26 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005953

dataset = DS005953(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 = DS005953(cache_dir="./data", subject="01")

Advanced query

dataset = DS005953(
    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{ds005953,
  title = {iEEG_visual},
  author = {Jonathan Winawer and Dora Hermes},
  doi = {10.18112/openneuro.ds005953.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005953.v1.0.0},
}

About This Dataset#

Information

This folder contains the ECoG data from 2 subjects performing a visual task used in the publications of Hermes et al., 2015, Cerebral Cortex “Stimulus Dependence of Gamma Oscillations in Human Visual Cortex” and Hermes et al., 2017, PLOS Biology “Neuronal synchrony and the relation between the blood-oxygen-level dependent response and the local field potential”. Contact: Dora Hermes (dorahermes@gmail.com)

Citing this dataset

View full README

Information

This folder contains the ECoG data from 2 subjects performing a visual task used in the publications of Hermes et al., 2015, Cerebral Cortex “Stimulus Dependence of Gamma Oscillations in Human Visual Cortex” and Hermes et al., 2017, PLOS Biology “Neuronal synchrony and the relation between the blood-oxygen-level dependent response and the local field potential”. Contact: Dora Hermes (dorahermes@gmail.com)

Citing this dataset

If you use this data as a part of any publications, please use the following citation: [1] Hermes D, Miller KJ, Wandell BA, Winawer J (2015). Stimulus dependence of gamma oscillations in human visual cortex. Cerebral Cortex 25(9):2951-9. https://doi.org/10.1093/cercor/bhu091 [2] Hermes D, Nguyen M, Winawer J. (2017). Neuronal synchrony and the relation between the BOLD response and the local field potential. PLOS Biology 15(7). https://doi.org/10.1371/journal.pbio.2001461

This dataset was made available with the support of the Netherlands Organization for Scientific Research www.nwo.nl under award number 016.VENI.178.048 to Dora Hermes and the National Institute Of Mental Health of the National Institutes of Health under Award Number R01MH111417 to Natalia Petridou and Jonathan Winawer. The ECoG data collection was facilitated by the Stanford Human Intracranial Cognitive Electrophysiology Program (SHICEP).

License

This dataset is made available under the Public Domain Dedication and License \nv1.0, whose full text can be found at \nhttp://www.opendatacommons.org/licenses/pddl/1.0/.

Task Description

Subjects were presented with images presented on a computer screen. The images spanned about 25x25 degrees of visual angle. Subjects fixated on a dot in the center of the screen that alternated between red and green, changing colors at random times. Subject 1 pressed a button when the fixation dot changed color. Subject 2 fixated on the dot but did not make manual responses because these responses were found to interfere with visual fixation.

Dataset and Stimuli

This data is organized according to the Brain Imaging Data Structure specification. A community- driven specification for organizing neurophysiology data along with its metadata. For more information on this data specification, see https://bids-specification.readthedocs.io/en/stable/

Each subject has their own folder (e.g., sub-01) which contains the raw EcoG data for that subject, as well as the metadata needed to understand the raw data and event timing. In addition, the stimuli/ folder contains the .png files of the presented images.

Stimuli

Stimuli including high contrast vertical gratings (0.16, 0.33, 0.65, or 1.3 duty cycles per degree square wave) and noise patterns (spectral power distributions of k/f^4; k/f^2; and k/f^0).

Raw data

Raw data is stored with the Brainvision data format. This can be read in to memory with the following tools:

Python: The pybv package (bids-standard/pybv)
Matlab: BrainVision analyzer (https://www.mathworks.com/products/connections/product_detail/brainvision-analyzer.html)

Dataset Information#

Dataset ID	`DS005953`
Title	iEEG_visual
Year	2025
Authors	Jonathan Winawer, Dora Hermes
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005953.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: 2
Recordings: 26
Tasks: 1

Channels & sampling rate

Channels: 96 (4), 118 (2)
Sampling rate (Hz): 1525.9 (4), 3051.76 (2)
Duration (hours): 0.0

API Reference#

Use the DS005953 class to access this dataset programmatically.

class eegdash.dataset.DS005953(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005953. Modality: ieeg; Experiment type: Perception; Subject type: Surgery. Subjects: 2; recordings: 3; 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/ds005953 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005953

Examples

>>> from eegdash.dataset import DS005953
>>> dataset = DS005953(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