EEGdash›OpenNeuro›DS003670

Iss. 3670 · 25 subjects · 62 recordings · CC0

Dataset Brief · Dataset of Concurrent EEG, ECG, and Behavior with Multiple Do…

DS003670: eeg dataset, 25 subjects#

Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation - BIDS

Citation: Nigel Gebodh, Zeinab Esmaeilpour, Abhishek Datta, Marom Bikson (20). Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation - BIDS. 10.18112/openneuro.ds003670.v1.1.0

25-participant EEG dataset — Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation - BIDS.

Data & curation Nigel Gebodh · Zeinab Esmaeilpour · Abhishek Datta · Marom Bikson
Year 20 · Distributed via OpenNeuro
Funding X (formerly Google X) · NIH R01NS095123 · + 5 more

EEG · 35 ch2000 HzBIDS 1.1.1Task · GXtESCTT6 sessionsHealthyVisualClinical/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 DS003670

dataset = DS003670(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 = DS003670(cache_dir="./data", subject="01")

Advanced query

dataset = DS003670(
    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{ds003670,
  title = {Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation - BIDS},
  author = {Nigel Gebodh and Zeinab Esmaeilpour and Abhishek Datta and Marom Bikson},
  doi = {10.18112/openneuro.ds003670.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds003670.v1.1.0},
}

§ 02Study · The README

About This Dataset#

This is the GX dataset formatted to comply with BIDS standard format.

The tES/EEG/CTT/Vigilance experiment contains 19 unique participants (some repeated experiments). Over a 70 min period EEG/ECG/EOG were recorded concurrently with a CTT where participants maintained a ball at the center of the screen and were periodically stimulated (with low-intensity noninvasive brain stimulation) for 30 secs with combinations of 9 stimulation montages. For the raw data please see: https://zenodo.org/record/4456079 For methodological details please see corresponding article titled:

Dataset of concurrent EEG, ECG, and behavior with multiple doses of transcranial Electrical Stimulation

We present a dataset combining human-participant high-density electroencephalography (EEG) with physiological and continuous behavioral metrics during transcranial electrical stimulation (tES). Data include within participant application of nine High-Definition tES (HD-tES) types, targeting three cortical regions (frontal, motor, parietal) with three stimulation waveforms (DC, 5 Hz, 30 Hz); more than 783 total stimulation trials over 62 sessions with EEG, physiological (ECG, EOG), and continuous behavioral vigilance/alertness metrics. Experiment 1 and 2 consisted of participants performing a continuous vigilance/alertness task over three 70-minute and two 70.5-minute sessions, respectively. Demographic data were collected, as well as self-reported wellness questionnaires before and after each session. Participants received all 9 stimulation types in Experiment 1, with each session including three stimulation types, with 4 trials per type. Participants received 2 stimulation types in Experiment 2, with 20 trials of a given stimulation type per session. Within-participant reliability was tested by repeating select sessions. This unique dataset supports a range of hypothesis testing including interactions of tDCS/tACS location and frequency, brain-state, physiology, fatigue, and cognitive performance.

For more details please see the full data descriptor article. Code used to import and process this dataset can be found here: GitHub : ngebodh/GX_tES_EEG_Physio_Behavior For downsampled data please see: Experiment 1 : https://doi.org/10.5281/zenodo.3840615 Experiment 2 : https://doi.org/10.5281/zenodo.3840617 - Nigel Gebodh (May 26th, 2021)

Synopsis

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=25, range 19–43 yr, mean 28.8 yr)

152025303540

Other · 25

Sex composition

Female
8
Male
18
F : M ratio
0.44 : 1

31% female · n = 26 subjects with reported sex.

HandednessRight · 19Left · 7

Channel counts: 35 ch (n=62 recordings)

Sampling frequencies: 2000.0 Hz (n=62 recordings)

Total recording duration: 72 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 35 ch · EEG · 2000 Hz · 25 subjects, 62 recordings

Live trace viewer — sub-021 · ses-02 · task-GXtESCTT

Showing one representative recording out of 25 subjects and 62 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.

Electrode layout — EEG · 35 sensors — 35 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.DS003670(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Gebodh2021

Canonical—

Importable asDS003670 · Gebodh2021

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

class eegdash.dataset.DS003670(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation - BIDS

Study:: ds003670 (OpenNeuro)
Author (year):: Gebodh2021
Canonical:: —

Also importable as: DS003670, Gebodh2021.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. Subjects: 25; recordings: 62; 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/ds003670 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003670 DOI: https://doi.org/10.18112/openneuro.ds003670.v1.1.0 NEMAR citation count: 6

Examples

>>> from eegdash.dataset import DS003670
>>> dataset = DS003670(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/ds003670 · pull with datasets.load_dataset("EEGDash/ds003670").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS003670.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 ds003670 to reproduce the tutorial on this dataset.

Citation

Nigel Gebodh, Zeinab Esmaeilpour, Abhishek Datta, Marom Bikson (20). Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation - BIDS. 10.18112/openneuro.ds003670.v1.1.0

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds003670.v1.1.0.

Related & sibling datasets

DS005688EEG · 20 subj DS007314EEG · 2 subj DS007315EEG · 2 subj DS006036EEG · 88 subj DS006260EEG · 76 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.1.1

Sidecars

events · events.json · channels · electrodes · coordsystem · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds003670.v1.1.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS003670`
Title	Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation - BIDS
Author (year)	`Gebodh2021`
Canonical	—
Importable as	`DS003670`, `Gebodh2021`
Year	20
Authors	Nigel Gebodh, Zeinab Esmaeilpour, Abhishek Datta, Marom Bikson
License	CC0
Citation / DOI	10.18112/openneuro.ds003670.v1.1.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS003670: eeg dataset, 25 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#