DS006065: ieeg dataset, 7 subjects#

Name: TSS_iEEG
Published: 2025
License: CC0

TSS_iEEG

Access recordings and metadata through EEGDash.

Citation: James Kragel, Joel Voss (2025). TSS_iEEG. 10.18112/openneuro.ds006065.v1.0.0

Modality: ieeg Subjects: 7 Recordings: 45 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006065

dataset = DS006065(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 = DS006065(cache_dir="./data", subject="01")

Advanced query

dataset = DS006065(
    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{ds006065,
  title = {TSS_iEEG},
  author = {James Kragel and Joel Voss},
  doi = {10.18112/openneuro.ds006065.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006065.v1.0.0},
}

About This Dataset#

iEEG Dataset: Theta-synchronized Stimulation of Human Hippocampal Networks

Information

This folder contains intracranial EEG (iEEG) data from 7 participants undergoing closed-loop stimulation as part of a study on hippocampal network connectivity, as used in the following publication: Kragel et al., 2025, Nature Communications: “Closed-loop control of theta oscillations enhances human hippocampal network connectivity” For questions or further information, contact:

Dataset Information#

Dataset ID	`DS006065`
Title	TSS_iEEG
Author (year)	`Kragel2025`
Canonical	—
Importable as	`DS006065`, `Kragel2025`
Year	2025
Authors	James Kragel, Joel Voss
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006065.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: 7
Recordings: 45
Tasks: 10

Channels & sampling rate

Channels: 168 (15), 175 (10), 82 (5), 68 (5), 181 (5), 43 (5)
Sampling rate (Hz): 500.0
Duration (hours): 10.699191111111112

API Reference#

Use the DS006065 class to access this dataset programmatically.

class eegdash.dataset.DS006065(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

TSS_iEEG

Study:: ds006065 (OpenNeuro)
Author (year):: Kragel2025
Canonical:: —

Also importable as: DS006065, Kragel2025.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Surgery. Subjects: 7; recordings: 45; tasks: 10.

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/ds006065 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006065 DOI: https://doi.org/10.18112/openneuro.ds006065.v1.0.0

Examples

>>> from eegdash.dataset import DS006065
>>> dataset = DS006065(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