EEGdash›OpenNeuro›DS006065

Iss. 6065 · 7 subjects · 45 recordings · CC0

Dataset Brief · TSS_iEEG

DS006065: ieeg dataset, 7 subjects#

TSS_iEEG

Citation: James Kragel, Joel Voss (20). TSS_iEEG. 10.18112/openneuro.ds006065.v1.0.0

7-participant iEEG dataset — TSS_iEEG.

Data & curation James Kragel · Joel Voss
Year 20 · Distributed via OpenNeuro
Funding This research was funded by the National Institute Of Neurological Disorders and Stroke under Award Number R01NS113804

iEEG · 168 (15), 175 (10), 181 (5), 82 (5), 68 (5), 43 (5) ch500 HzBIDS 1.810 tasksSurgeryOtherClinical/Intervention

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 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},
}

§ 02Study · The README

About This Dataset#

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

This dataset is made available under the **Public Domain Dedication and License v1.0**.

iEEG Dataset: Theta-synchronized Stimulation of Human Hippocampal Networks

Information

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.

View full README

iEEG Dataset: Theta-synchronized Stimulation of Human Hippocampal Networks

Information

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: - MATLAB: FieldTrip toolbox - Python: pybv package <bids-standard/pybv>\`__

Electrode Coordinates

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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

436882168175181

Sampling frequencies: 500.0 Hz (n=45 recordings)

Total recording duration: 10 h 41 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 168 (15), 175 (10), 181 (5), 82 (5), 68 (5), 43 (5) ch · iEEG · 500 Hz · 7 subjects, 45 recordings

Live trace viewer — sub-p18 · task-restpre

Showing one representative recording out of 7 subjects and 45 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 · 175 sensors — 175 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.DS006065(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Kragel2025

Canonical—

Importable asDS006065 · Kragel2025

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

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

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: 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/ds006065 · pull with datasets.load_dataset("EEGDash/ds006065").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS006065.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 ds006065 to reproduce the tutorial on this dataset.

Citation

James Kragel, Joel Voss (20). TSS_iEEG. 10.18112/openneuro.ds006065.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.ds006065.v1.0.0.

Related & sibling datasets

DS004457IEEG · 5 subj DS004819IEEG · 1 subj DS007445IEEG · 19 subj DS003029IEEG · 35 subj DS004100IEEG · 57 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.8

Sidecars

channels · electrodes · coordsystem

Provenance

CC0 · 10.18112/openneuro.ds006065.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS006065`
Title	TSS_iEEG
Author (year)	`Kragel2025`
Canonical	—
Importable as	`DS006065`, `Kragel2025`
Year	20
Authors	James Kragel, Joel Voss
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006065.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS006065: ieeg dataset, 7 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#