DS003670#

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

Access recordings and metadata through EEGDash.

Citation: Nigel Gebodh, Zeinab Esmaeilpour, Abhishek Datta, Marom Bikson (2021). Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation - BIDS. 10.18112/openneuro.ds003670.v1.1.0

Modality: eeg Subjects: 25 Recordings: 512 License: CC0 Source: openneuro Citations: 6.0

Metadata: Complete (100%)

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},
}

About This Dataset#

Synopsis 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

Data Descriptor Abstract 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)

Dataset Information#

Dataset ID	`DS003670`
Title	Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation - BIDS
Year	2021
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

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: 25
Recordings: 512
Tasks: 1

Channels & sampling rate

Channels: 32 (62), 35 (62)
Sampling rate (Hz): 2000.0
Duration (hours): 0.0

API Reference#

Use the DS003670 class to access this dataset programmatically.

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

Bases: EEGDashDataset

OpenNeuro dataset ds003670. Modality: eeg; Experiment type: Attention. 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

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, 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