DS005121#

Siefert2024

Access recordings and metadata through EEGDash.

Citation: Elizabeth M. Siefert, Sindhuja Uppuluri, Jianing Mu, Marlie C. Tandoc, James W. Antony, Anna C. Schapiro (2024). Siefert2024. 10.18112/openneuro.ds005121.v1.0.2

Modality: eeg Subjects: 34 Recordings: 272 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005121

dataset = DS005121(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 = DS005121(cache_dir="./data", subject="01")

Advanced query

dataset = DS005121(
    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{ds005121,
  title = {Siefert2024},
  author = {Elizabeth M. Siefert and Sindhuja Uppuluri and Jianing Mu and Marlie C. Tandoc and James W. Antony and Anna C. Schapiro},
  doi = {10.18112/openneuro.ds005121.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds005121.v1.0.2},
}

About This Dataset#

Overview: This is the “Siefert2024” dataset. It is the sleep EEG data from Siefert et al., 2024 (https://doi.org/10.1523/JNEUROSCI.0022-24.2024). In brief, it contains sleep EEG data from 34 participants while Targeted Memory Reactivation was administered.

Please cite the following paper: E.M. Siefert, S. Uppuluri, J. Mu., M.C. Tandoc, J.W. Antony, A.C. Schapiro (2024). Memory reactivation during sleep does not act holistically on object memory. Journal of Neuroscience, 10.1523/JNEUROSCI.0022-24.2024

The dataset is formatted according to the Brain Imaging Data Structure (BIDS). Data organization was performed using FieldTrip data2bids function.

Additional details: Events.tsv files contain information about the different cueing events. - item_value is the sound index number used by the TMR system. This number corresponds to the literal code name of the satellite (i.e., “nivex” or “sorex”). Here, 33 indicates no sound was played but a SO was tagged. - SatNum is the corresponding satellite number. This number is the same satellite number that is used in the behavioral data.

0 indicates no sound was played.

Satellites 1-5 are the studied satellites from the blocked category.

Satellites 6-10 are the studied satellites from the interleaved category.

Satellites 11-15 are the studied satellites from the uncued category.

System crashed and had to be restarted for participants 6, 7, 15, 17, 21, resulting in two EEG files for these participants.

Please contact Liz Siefert (sieferte@pennmedicine.upenn.edu) with any additional questions.

Dataset Information#

Dataset ID	`DS005121`
Title	Siefert2024
Year	2024
Authors	Elizabeth M. Siefert, Sindhuja Uppuluri, Jianing Mu, Marlie C. Tandoc, James W. Antony, Anna C. Schapiro
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005121.v1.0.2
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: 34
Recordings: 272
Tasks: 1

Channels & sampling rate

Channels: 58 (39), 65 (39)
Sampling rate (Hz): 512.0
Duration (hours): 0.0

API Reference#

Use the DS005121 class to access this dataset programmatically.

class eegdash.dataset.DS005121(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005121. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 34; recordings: 39; 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/ds005121 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005121

Examples

>>> from eegdash.dataset import DS005121
>>> dataset = DS005121(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