EEGdash›OpenNeuro›DS005688

Iss. 5688 · 20 subjects · 89 recordings · CC0

Dataset Brief · visStim

DS005688: eeg dataset, 20 subjects#

visStim

Citation: Henry Tan, Devon Griggs, Lucas Chen, Kahte Culevski, Kat Floerchinger, Alissa Phutirat, Gabe Koh, Nels Schimek, Pierre Mourad (2024). visStim. 10.18112/openneuro.ds005688.v1.0.1

20-participant EEG dataset — visStim.

Data & curation Henry Tan · Devon Griggs · Lucas Chen · Kahte Culevski · Kat Floerchinger · Alissa Phutirat · …
Year 2024 · Distributed via OpenNeuro
Funding R01 NS119395

EEG · 5 (86), 1 (3) ch10000 Hz · mixedBIDS 1.8.05 tasksHealthyVisualClinical/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 DS005688

dataset = DS005688(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 = DS005688(cache_dir="./data", subject="01")

Advanced query

dataset = DS005688(
    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{ds005688,
  title = {visStim},
  author = {Henry Tan and Devon Griggs and Lucas Chen and Kahte Culevski and Kat Floerchinger and Alissa Phutirat and Gabe Koh and Nels Schimek and Pierre Mourad},
  doi = {10.18112/openneuro.ds005688.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005688.v1.0.1},
}

§ 02Study · The README

About This Dataset#

This dataset was collected for the study “Diagnostic ultrasound enhances, then reduces, exogenously induced brain activity of mice” by Tan et al. (2024). The research demonstrates how transcranially delivered diagnostic ultrasound (tDUS) modulates the brain’s receptivity to external stimuli, using a blinking light stimulus in a mouse model.

The study findings highlight the potential for diagnostic ultrasound to intentionally modulate brain function, paving the way for possible future clinical and therapeutic applications.

Please cite the following paper when using this dataset:

Dataset description

Tan H, Griggs DJ, Chen L, et al. Diagnostic ultrasound enhances, then reduces, exogenously induced brain activity of mice. Frontiers in Neuroscience. 2024. DOI: [in peer review].

License

This dataset is proprietary to the Department of Neurological Surgery, University of Washington, Seattle, WA, USA.

Usage is restricted to academic and non-commercial research. Redistribution, modification, or commercial use is prohibited without prior permission. For inquiries, contact: Pierre D. Mourad (doumitt@uw.edu).

View full README

Dataset description

Tan H, Griggs DJ, Chen L, et al. Diagnostic ultrasound enhances, then reduces, exogenously induced brain activity of mice. Frontiers in Neuroscience. 2024. DOI: [in peer review].

License

This dataset is proprietary to the Department of Neurological Surgery, University of Washington, Seattle, WA, USA.

Usage is restricted to academic and non-commercial research. Redistribution, modification, or commercial use is prohibited without prior permission. For inquiries, contact: Pierre D. Mourad (doumitt@uw.edu).

Acknowledgements

We thank the Department of Neurological Surgery, University of Washington, for internal funding.

This research was supported by R01 NS119395 and P51 OD010425 (DJG) and the Mary Gates Research Scholarship** (HT).

Dataset Overview

This dataset comprises electrocorticography (ECoG) recordings from three cohorts of C57BL/6 mice exposed to combinations of diagnostic ultrasound (tDUS) and blinking light stimuli. The study investigates how tDUS influences the visual cortex’s response to external visual stimulation.

Key Features: - Subjects: 20 C57BL/6 mice divided into three experimental cohorts:

Light-only cohort: Exposed to blinking light only.

US-only cohort: Exposed to tDUS without light.

US+Light cohort: Exposed to blinking light combined with tDUS.

Electrode Placement: ECoG electrodes targeted visual and somatosensory cortices.

Recording Conditions: Data recorded at 20 kS/s, filtered (5–55 Hz), and analyzed with MATLAB.

Data Structure

Data Files: - Raw ECoG Data: Continuous brain activity recordings. - Event-Triggered Data: Segment-specific RMS values for blinking light events. - Processed Data: Filtered and normalized brain activity traces.

Metadata: - Experimental conditions, cohort allocation, and baseline brain activity.

Methodology

Stimulation Protocols: Mice were exposed to blinking light stimuli (10 seconds per event) and tDUS delivered through a P21x5-1 scan head (Sonosite MicroMaxx system).

Data Collection: Baseline and event-triggered ECoG signals were recorded using LabChart software.

Analysis: RMS values normalized to baseline activity for comparative statistical analysis.

Outcomes Key Findings: 1. Enhanced Brain Activity: Simultaneous tDUS and blinking light increased cortical activity compared to light alone. 2. Persistent Effects: tDUS effects on brain activity persisted after stimulation ceased. 3. No Effect of tDUS Alone: tDUS without light did not activate cortical activity but reduced subsequent activity.

References For a complete list of references, please consult the manuscript.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

15

Sampling frequencies (Hz)

1000020000

Total recording duration: 55 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 5 (86), 1 (3) ch · EEG · 10000 Hz · mixed · 20 subjects, 89 recordings

Live trace viewer — sub-13 · ses-01 · task-baseline

Showing one representative recording out of 20 subjects and 89 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

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.DS005688(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Tan2024

Canonical—

Importable asDS005688 · Tan2024

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

class eegdash.dataset.DS005688(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

visStim

Study:: ds005688 (OpenNeuro)
Author (year):: Tan2024
Canonical:: —

Also importable as: DS005688, Tan2024.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. Subjects: 20; recordings: 89; tasks: 5.

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/ds005688 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005688 DOI: https://doi.org/10.18112/openneuro.ds005688.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005688
>>> dataset = DS005688(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/ds005688 · pull with datasets.load_dataset("EEGDash/ds005688").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS005688.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 ds005688 to reproduce the tutorial on this dataset.

Citation

Henry Tan, Devon Griggs, Lucas Chen, Kahte Culevski, Kat Floerchinger, … (2024). visStim. 10.18112/openneuro.ds005688.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds005688.v1.0.1.

Related & sibling datasets

DS003670EEG · 25 subj DS007314EEG · 2 subj DS007315EEG · 2 subj DS006036EEG · 88 subj DS006260EEG · 76 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.8.0

Sidecars

eeg.json

Provenance

CC0 · 10.18112/openneuro.ds005688.v1.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS005688`
Title	visStim
Author (year)	`Tan2024`
Canonical	—
Importable as	`DS005688`, `Tan2024`
Year	2024
Authors	Henry Tan, Devon Griggs, Lucas Chen, Kahte Culevski, Kat Floerchinger, Alissa Phutirat, Gabe Koh, Nels Schimek, Pierre Mourad
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005688.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS005688: eeg dataset, 20 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#