DS005121: eeg dataset, 34 subjects#
Siefert2024
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
34-participant EEG dataset — Siefert2024.
Quickstart#
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)
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'])
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.
Cohort#
Dataset Statistics#
Channel counts: 65 ch (n=39 recordings)
Sampling frequencies: 512.0 Hz (n=39 recordings)
Total recording duration: 40 h
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-Siefert2024 · run-01
Showing one representative recording out of
34 subjects and 39 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 · 58 sensors — 58 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
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.
Full dataset metadata table
Dataset ID |
|
Title |
Siefert2024 |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2024 |
Authors |
Elizabeth M. Siefert, Sindhuja Uppuluri, Jianing Mu, Marlie C. Tandoc, James W. Antony, Anna C. Schapiro |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005121 · Siefert2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005121(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Siefert2024
- Study:
ds005121(OpenNeuro)- Author (year):
Siefert2024- Canonical:
—
Also importable as:
DS005121,Siefert2024.Modality:
eeg. 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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 DOI: https://doi.org/10.18112/openneuro.ds005121.v1.0.2 NEMAR citation count: 1
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: 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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds005121").huggingface
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 ds005121 to reproduce the tutorial on this dataset.
Citation
Elizabeth M. Siefert, Sindhuja Uppuluri, Jianing Mu, Marlie C. Tandoc, James W. Antony, … (2024). Siefert2024. 10.18112/openneuro.ds005121.v1.0.2
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds005121.v1.0.2.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset