DS004635#

Gaffrey Lab Infant Microstates Reliability

Access recordings and metadata through EEGDash.

Citation: Armen Bagdasarov, Michael S. Gaffrey (2023). Gaffrey Lab Infant Microstates Reliability. 10.18112/openneuro.ds004635.v3.1.0

Modality: eeg Subjects: 48 Recordings: 292 License: CC0 Source: openneuro Citations: 2.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004635

dataset = DS004635(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 = DS004635(cache_dir="./data", subject="01")

Advanced query

dataset = DS004635(
    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{ds004635,
  title = {Gaffrey Lab Infant Microstates Reliability},
  author = {Armen Bagdasarov and Michael S. Gaffrey},
  doi = {10.18112/openneuro.ds004635.v3.1.0},
  url = {https://doi.org/10.18112/openneuro.ds004635.v3.1.0},
}

About This Dataset#

Participants were 48, 5-10-month-old infants (27 male). All research was approved by the Duke University Health System Institutional Review Board and carried out in accordance with the Declaration of Helsinki. Caregivers provided informed consent, and compensation was provided for their participation. Infants sat on their caregiver’s lap and watched up to 15 minutes of relaxing videos with sound (i.e., 10, 90-second videos separated by breaks during which caregivers could play with their infant). Before each video started, an attention grabber (i.e., three-second video of a noisy rattle) directed the infant’s attention to the screen. Videos were presented with E-Prime software (Psychological Software Tools, Pittsburgh, PA). Caregivers were instructed to silently sit still during videos. If infants shifted their attention away from the screen, caregivers were permitted to re-direct their attention only by pointing to the screen. EEG was recorded at 1000 Hertz (Hz) and referenced to the vertex (channel Cz) using a 128-channel HydroCel Geodesic Sensor Net (Electrical Geodesics, Eugene, OR). Impedances were maintained below 50 kilohms throughout the EEG session. For more information, visit: gaffreylab/EEG-Microstate-Analysis-Tutorial

Dataset Information#

Dataset ID	`DS004635`
Title	Gaffrey Lab Infant Microstates Reliability
Year	2023
Authors	Armen Bagdasarov, Michael S. Gaffrey
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004635.v3.1.0
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: 48
Recordings: 292
Tasks: 1

Channels & sampling rate

Channels: 129
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS004635 class to access this dataset programmatically.

class eegdash.dataset.DS004635(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004635. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 48; recordings: 48; 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/ds004635 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004635

Examples

>>> from eegdash.dataset import DS004635
>>> dataset = DS004635(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