DS004395#

Penn Electrophysiology of Encoding and Retrieval Study (PEERS)

Access recordings and metadata through EEGDash.

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 (2023). Penn Electrophysiology of Encoding and Retrieval Study (PEERS). 10.18112/openneuro.ds004395.v2.0.0

Modality: eeg Subjects: 364 Recordings: 6483 License: CC0 Source: openneuro Citations: 6.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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

Dataset Information#

Dataset ID	`DS004395`
Title	Penn Electrophysiology of Encoding and Retrieval Study (PEERS)
Year	2023
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	doi:10.18112/openneuro.ds004395.v2.0.0
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: 364
Recordings: 6483
Tasks: 3

Channels & sampling rate

Channels: 129 (4980), 125 (4978), 137 (1490), 128 (1455), 136 (37), 144 (11), 135 (10), 263 (2), 272 (2), 143
Sampling rate (Hz): 500.0 (9892), 2048.0 (2932), 512.0 (56), 250.0 (34), 1000.0 (30), 1024.0 (22)
Duration (hours): 0.0

API Reference#

Use the DS004395 class to access this dataset programmatically.

class eegdash.dataset.DS004395(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004395. 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

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/ds004395 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004395

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, 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