DS006065#

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: 250 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

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)

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'])

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:

View full README

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: - James Kragel: jkragel@uchicago.edu_ - Joel Voss: joelvoss@uchicago.edu_

License

This dataset is made available under the **Public Domain Dedication and License v1.0**. Full text: http://www.opendatacommons.org/licenses/pddl/1.0_

Dataset and Protocol

The data are organized according to the Brain Imaging Data Structure (BIDS) iEEG specification, a community-driven standard for organizing neurophysiology data along with its metadata.

Structure

Each subject folder contains the raw iEEG data for that subject, segmented into different periods of the stimulation protocol: - Pre-stimulation evoked potentials - Post-stimulation evoked potentials - Pre-stimulation rest - Post-stimulation rest - Closed-loop stimulation - Control stimulation

Raw Data

The raw data are stored in BrainVision format (vhdr, vmrk, and eeg files). You can read these files into memory using the following tools:

Electrode Coordinates

Electrode coordinates are provided in **MNI space**, registered to the **MNI152 2009c asymmetrical template**.

Dataset Information#

Dataset ID

DS006065

Title

TSS_iEEG

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
Copy-paste 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},
}

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: 250

  • Tasks: 10

Channels & sampling rate

  • Channels: 168 (30), 175 (20), 43 (10), 181 (10), 82 (10), 68 (10)

  • Sampling rate (Hz): 500.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: Other

  • Type: Clinical/Intervention

Files & format

  • Size on disk: 9.6 GB

  • File count: 250

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds006065.v1.0.0

Provenance

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

OpenNeuro dataset ds006065. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Unknown. 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

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

See Also#