DS004395: eeg dataset, 364 subjects#
Penn Electrophysiology of Encoding and Retrieval Study (PEERS)
Citation: Michael J. Kahana, Joseph H. Rudoler, Lynn J. Lohnas, Karl Healey, Ada Aka, Adam Broitman, Elizabeth Crutchley, Patrick Crutchley, Kylie H. Alm, Brandon S. Katerman, Nicole E. Miller, Joel R. Kuhn, Yuxuan Li, Nicole M. Long, Jonathan Miller, Madison D. Paron, Jesse K. Pazdera, Isaac Pedisich, Christoph T. Weidemann (2019). Penn Electrophysiology of Encoding and Retrieval Study (PEERS). 10.18112/openneuro.ds004395.v2.0.0
364-participant EEG dataset — Penn Electrophysiology of Encoding and Retrieval Study (PEERS).
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004395
dataset = DS004395(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004395(cache_dir="./data", subject="01")
Advanced query
dataset = DS004395(
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{ds004395,
title = {Penn Electrophysiology of Encoding and Retrieval Study (PEERS)},
author = {Michael J. Kahana and Joseph H. Rudoler and Lynn J. Lohnas and Karl Healey and Ada Aka and Adam Broitman and Elizabeth Crutchley and Patrick Crutchley and Kylie H. Alm and Brandon S. Katerman and Nicole E. Miller and Joel R. Kuhn and Yuxuan Li and Nicole M. Long and Jonathan Miller and Madison D. Paron and Jesse K. Pazdera and Isaac Pedisich and Christoph T. Weidemann},
doi = {10.18112/openneuro.ds004395.v2.0.0},
url = {https://doi.org/10.18112/openneuro.ds004395.v2.0.0},
}
About This Dataset#
The Penn Electrophysiology of Encoding and Retrieval Study (PEERS) aimed to characterize the behavioral and electrophysiological (EEG) correlates of memory encoding and retrieval in highly practiced individuals. Across five PEERS experiments, 300+ subjects contributed more than 7,000 90 minute memory testing sessions with recorded EEG data.
See the Computational Memory Lab’s wiki page for more detailed information, and this paper for a discussion of the main findings and lessons learned from this large-scale study.
This dataset contains 3 experiments: * ltpFR (a.k.a. PEERS1-3) * ltpFR2 (a.k.a. PEERS4) * VFFR (a.k.a. PEERS5)
Electroencephalogram (EEG) data were recorded with either a 129-channel Geodesic Sensor Net (either GSN 200 model or HydroCel GSN model) using the Netstation acquisition environment (Electrical Geodesics, Inc.; EGI) or with a 128-channel BioSemi headcap using the Biosemi ActiveTwo acquisition system. Note: subject-specific electrode layouts were NOT recorded. Despite being labeled as “CapTrak” space, the coordinates reflect a generic electrode layout for a given headcap and do NOT represent any individual’s head shape.
References
Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8
Cohort#
Dataset Statistics#
Age distribution by gender (n=329, range 17–86 yr, mean 27.0 yr)
Sex composition
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 9115 h
Signal · Electrodes & live trace#
Live trace viewer — sub-LTP238 · ses-19 · task-ltpFR
Showing one representative recording out of
364 subjects and 6483 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 · 125 sensors — 125 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 |
Penn Electrophysiology of Encoding and Retrieval Study (PEERS) |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2019 |
Authors |
Michael J. Kahana, Joseph H. Rudoler, Lynn J. Lohnas, Karl Healey, Ada Aka, Adam Broitman, Elizabeth Crutchley, Patrick Crutchley, Kylie H. Alm, Brandon S. Katerman, Nicole E. Miller, Joel R. Kuhn, Yuxuan Li, Nicole M. Long, Jonathan Miller, Madison D. Paron, Jesse K. Pazdera, Isaac Pedisich, Christoph T. Weidemann |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004395,
title = {Penn Electrophysiology of Encoding and Retrieval Study (PEERS)},
author = {Michael J. Kahana and Joseph H. Rudoler and Lynn J. Lohnas and Karl Healey and Ada Aka and Adam Broitman and Elizabeth Crutchley and Patrick Crutchley and Kylie H. Alm and Brandon S. Katerman and Nicole E. Miller and Joel R. Kuhn and Yuxuan Li and Nicole M. Long and Jonathan Miller and Madison D. Paron and Jesse K. Pazdera and Isaac Pedisich and Christoph T. Weidemann},
doi = {10.18112/openneuro.ds004395.v2.0.0},
url = {https://doi.org/10.18112/openneuro.ds004395.v2.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004395 · Kahana2023eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004395(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Penn Electrophysiology of Encoding and Retrieval Study (PEERS)
- Study:
ds004395(OpenNeuro)- Author (year):
Kahana2023- Canonical:
—
Also importable as:
DS004395,Kahana2023.Modality:
eeg; Experiment type:Memory; Subject type:Healthy. Subjects: 364; recordings: 6483; tasks: 3.- 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/ds004395 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004395 DOI: https://doi.org/10.18112/openneuro.ds004395.v2.0.0 NEMAR citation count: 6
Examples
>>> from eegdash.dataset import DS004395 >>> dataset = DS004395(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/ds004395").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 ds004395 to reproduce the tutorial on this dataset.
Citation
Michael J. Kahana, Joseph H. Rudoler, Lynn J. Lohnas, Karl Healey, Ada Aka, … (2019). Penn Electrophysiology of Encoding and Retrieval Study (PEERS). 10.18112/openneuro.ds004395.v2.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004395.v2.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset