EEGdash›OpenNeuro›DS005953

Iss. 5953 · 2 subjects · 3 recordings · CC0

Dataset Brief · iEEG_visual

DS005953: ieeg dataset, 2 subjects#

iEEG_visual

Citation: Jonathan Winawer, Dora Hermes (2015). iEEG_visual. 10.18112/openneuro.ds005953.v1.0.0

2-participant iEEG dataset — iEEG_visual.

Data & curation Jonathan Winawer · Dora Hermes
Year 2015 · Distributed via OpenNeuro
Funding This research has received funding from the National Institute Of Mental Health of the National Institutes of Health under Award Number R01MH111417

iEEG · 96 (2), 118 ch1526, 3052 HzBIDS 1.0.2Task · visualSurgeryVisualPerception

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#

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},
}

§ 02Study · The README

About This Dataset#

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)

Information

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

View full README

Information

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)

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

96118

Sampling frequencies (Hz)

1525.93051.8

Total recording duration: 11 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 96 (2), 118 ch · iEEG · 1526, 3052 Hz · 2 subjects, 3 recordings

Live trace viewer — sub-01 · ses-01 · task-visual · run-01

Showing one representative recording out of 2 subjects and 3 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _ieeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?ieeg=<url>) to inspect it.

Electrode layout — iEEG · 96 sensors — 96 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.

HED event descriptors word cloud

§ 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…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS005953(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Winawer2025

Canonical—

Importable asDS005953 · Winawer2025

Sourceeegdash/dataset/registry.py · [source ↗]

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

iEEG_visual

Study:: ds005953 (OpenNeuro)
Author (year):: Winawer2025
Canonical:: —

Also importable as: DS005953, Winawer2025.

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 DOI: https://doi.org/10.18112/openneuro.ds005953.v1.0.0

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: 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/ds005953 · pull with datasets.load_dataset("EEGDash/ds005953").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS005953.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 ds005953 to reproduce the tutorial on this dataset.

Citation

Jonathan Winawer, Dora Hermes (2015). iEEG_visual. 10.18112/openneuro.ds005953.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.ds005953.v1.0.0.

Related & sibling datasets

DS004194IEEG · 14 subj DS006392IEEG · 1 subj DS005558IEEG · 7 subj DS005523IEEG · 21 subj DS004865IEEG · 42 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.0.2

Sidecars

events · channels · electrodes · coordsystem

Provenance

CC0 · 10.18112/openneuro.ds005953.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS005953`
Title	iEEG_visual
Author (year)	`Winawer2025`
Canonical	—
Importable as	`DS005953`, `Winawer2025`
Year	2015
Authors	Jonathan Winawer, Dora Hermes
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005953.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS005953: ieeg dataset, 2 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#