DS004865#

pyFR: Delayed Free Recall of Word Lists, Preliminary Cognitive Electrophysiology Study

Access recordings and metadata through EEGDash.

Citation: Haydn G. Herrema, Michael J. Kahana (2023). pyFR: Delayed Free Recall of Word Lists, Preliminary Cognitive Electrophysiology Study. 10.18112/openneuro.ds004865.v2.0.1

Modality: ieeg Subjects: 49 Recordings: 1244 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004865

dataset = DS004865(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 = DS004865(cache_dir="./data", subject="01")

Advanced query

dataset = DS004865(
    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{ds004865,
  title = {pyFR: Delayed Free Recall of Word Lists, Preliminary Cognitive Electrophysiology Study},
  author = {Haydn G. Herrema and Michael J. Kahana},
  doi = {10.18112/openneuro.ds004865.v2.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004865.v2.0.1},
}

About This Dataset#

pyFR: Delayed Free Recall of Word Lists, Preliminary Cognitive Electrophysiology Study

Description

This dataset contains behavioral events and intracranial electrophysiological recordings from a delayed free recall task. The experiment consists of participants studying a list of words, presented visually one at a time, completing simple arithmetic problems that function as a distractor, and then freely recalled the words from the just-presented list in any order. The data was collected at clinical sites across the country as part of a collaboration with the Computational Memory Lab at the University of Pennsylvania.

This study was a preliminary cogntive electrophysiology study undertaken by the Computational Memory Lab, and is a predecessor to the following datasets: FR1_ & CatFR1_

To Note

The iEEG recordings are labeled either “monopolar” or “bipolar.” The monopolar recordings are referenced (typically a mastoid reference), but should always be re-referenced before analysis. The bipolar recordings are referenced according to a paired scheme indicated by the accompanying bipolar channels tables.
Each subject has a unique montage of electrode locations. MNI and Talairach coordinates are provided when available, along with brain region annotations.
Recordings were made on multiple different systems, so we have done the scaling to provide all voltage values in V.

Contact

For questions or inquiries, please contact sas-kahana-sysadmin@sas.upenn.edu.

Dataset Information#

Dataset ID	`DS004865`
Title	pyFR: Delayed Free Recall of Word Lists, Preliminary Cognitive Electrophysiology Study
Year	2023
Authors	Haydn G. Herrema, Michael J. Kahana
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004865.v2.0.1
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: 49
Recordings: 1244
Tasks: 1

Channels & sampling rate

Channels: 100 (14), 74 (10), 80 (10), 131 (10), 62 (8), 108 (8), 46 (8), 53 (8), 86 (8), 85 (8), 54 (8), 110 (8), 55 (6), 42 (6), 121 (6), 150 (6), 47 (6), 116 (6), 32 (6), 104 (6), 70 (6), 96 (6), 123 (6), 48 (6), 168 (6), 105 (6), 88 (6), 72 (6), 91 (6), 82 (6), 27 (6), 109 (6), 84 (6), 75 (6), 78 (6), 124 (4), 102 (4), 36 (4), 52 (4), 57 (4), 68 (4), 126 (4), 63 (4), 130 (4), 142 (4), 153 (4), 119 (4), 87 (4), 144 (4), 149 (4), 58 (4), 111 (4), 76 (4), 98 (2), 94 (2), 56 (2), 81 (2), 90 (2), 95 (2), 187 (2), 64 (2), 97 (2), 101 (2), 120 (2), 203 (2), 160 (2)
Sampling rate (Hz): 1000.0 (204), 512.0 (80), 2000.0 (32), 400.0 (16), 499.7071 (12)
Duration (hours): 0.0

API Reference#

Use the DS004865 class to access this dataset programmatically.

class eegdash.dataset.DS004865(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004865. Modality: ieeg; Experiment type: Memory; Subject type: Surgery. Subjects: 42; recordings: 172; 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/ds004865 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004865

Examples

>>> from eegdash.dataset import DS004865
>>> dataset = DS004865(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