DS005169#

Dataset of intracranial EEG during cortical stimulation evoking visual effects

Access recordings and metadata through EEGDash.

Citation: Andrei Barborica, Felicia Mihai, Laurentiu Tofan, Irina Oane, Ioana Mindruta (2024). Dataset of intracranial EEG during cortical stimulation evoking visual effects. 10.18112/openneuro.ds005169.v1.0.0

Modality: ieeg Subjects: 20 Recordings: 1165 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005169

dataset = DS005169(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 = DS005169(cache_dir="./data", subject="01")

Advanced query

dataset = DS005169(
    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{ds005169,
  title = {Dataset of intracranial EEG during cortical stimulation evoking visual effects},
  author = {Andrei Barborica and Felicia Mihai and Laurentiu Tofan and Irina Oane and Ioana Mindruta},
  doi = {10.18112/openneuro.ds005169.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005169.v1.0.0},
}

About This Dataset#

In this dataset we included iEEG recordings of responses to 115 intracranial high frequency stimulations evoking visual hallucinations, in 22 patients undergoing stereo-EEG presurgical evaluation for drug-resistant epilepsy.

The dataset contains 21 seconds of iEEG data around each stimulation, 8 seconds before the start of the stimulation, up to 5 seconds of intracranial stimulation and 8 seconds after the end of the stimulation.

We have used high-frequency bipolar stimulations of different areas of the brain, using alternating polarity biphasic pulses having a duration of 1 ms, at 43.2 Hz or 50 Hz, current intensity between 0.25 to 3 mA, for up to 5 s. Alternating polarity protocol allows disambiguating neuronal responses time-locked to the stimulation pulses from the artefactual components, according to Barborica et al., 2022 (doi: 10.1002/hbm.25749). It is therefore possible to identify the brain networks underlying the clinical effects, and to create symptom-related activation/connectivity maps.

The contact pair on which stimulation is applied, the current intensity level and evoked effect are specified in the events tsv. The responses are classified in 14 clinical categories: elementary (unstructured flashes of light), plus hallucination (presence of light in different forms or colors overlaying the background vision), minus hallucination (negative elementary phenomena described as scotoma, quadrantanopia, hemianopia or amaurosis), static, dynamic, continuous hallucination, intermittent hallucination, peripheric, central, whole visual field, color, non-color, combined visual symptoms, multimodal hallucinations.

Not all patients in which stimulations evoked visual hallucinations met the inclusion criteria for network analysis that requires running the freesurfer pipeline, for instance patients having prior resections, therefore there are subjects that do not contain ieeg data. However, they were kept in order to match the number of patients in the companion manuscript.

Contact: andrei.barborica@fizica.unibuc.ro

Dataset Information#

Dataset ID	`DS005169`
Title	Dataset of intracranial EEG during cortical stimulation evoking visual effects
Year	2024
Authors	Andrei Barborica, Felicia Mihai, Laurentiu Tofan, Irina Oane, Ioana Mindruta
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005169.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: 20
Recordings: 1165
Tasks: 1

Channels & sampling rate

Channels: 82 (38), 94 (18), 101 (12), 102 (10), 95 (10), 136 (10), 193 (10), 70 (8), 40 (8), 184 (6), 85 (6), 83 (6), 84 (6), 79 (6), 143 (4), 106 (4), 91 (4), 104 (4), 58 (4), 92 (2), 99 (2), 105 (2), 39 (2), 144 (2), 80 (2), 100 (2), 96 (2), 88 (2), 71 (2), 113 (2), 103 (2), 89 (2), 86 (2), 188 (2), 114 (2), 81 (2), 38 (2), 109 (2), 76 (2), 98 (2), 69 (2), 186 (2), 29 (2), 160 (2)
Sampling rate (Hz): 4096.0
Duration (hours): 0.0

API Reference#

Use the DS005169 class to access this dataset programmatically.

class eegdash.dataset.DS005169(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005169. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 20; recordings: 112; 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/ds005169 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005169

Examples

>>> from eegdash.dataset import DS005169
>>> dataset = DS005169(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