EEGDash/ds004865

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

Dataset ID: ds004865

Herrema2023_pyFR_Delayed_Free

Canonical aliases: pyFR

At a glance: IEEG · Visual memory · surgery · 42 subjects · 172 recordings · CC0

Load this dataset

This repo is a pointer. The raw EEG data lives at its canonical source (OpenNeuro / NEMAR); EEGDash streams it on demand and returns a PyTorch / braindecode dataset.

# pip install eegdash
from eegdash import EEGDashDataset

ds = EEGDashDataset(dataset="ds004865", cache_dir="./cache")
print(len(ds), "recordings")

You can also load it by canonical alias — these are registered classes in eegdash.dataset:

from eegdash.dataset import pyFR
ds = pyFR(cache_dir="./cache")

If the dataset has been mirrored to the HF Hub in braindecode's Zarr layout, you can also pull it directly:

from braindecode.datasets import BaseConcatDataset
ds = BaseConcatDataset.pull_from_hub("EEGDash/ds004865")

Dataset metadata

Subjects	42
Age range	15–57 yrs, mean 34.1
Recordings	172
Tasks (count)	1
Sessions	5
Channels	100 (×7), 80 (×5), 74 (×5), 131 (×5), 46 (×4), 108 (×4), 62 (×4), 110 (×4), 54 (×4), 85 (×4), 86 (×4), 53 (×4), 32 (×3), 116 (×3), 47 (×3), 150 (×3), 121 (×3), 42 (×3), 55 (×3), 75 (×3), 78 (×3), 84 (×3), 109 (×3), 27 (×3), 82 (×3), 91 (×3), 72 (×3), 88 (×3), 105 (×3), 168 (×3), 48 (×3), 123 (×3), 96 (×3), 70 (×3), 104 (×3), 130 (×2), 63 (×2), 126 (×2), 68 (×2), 57 (×2), 52 (×2), 36 (×2), 102 (×2), 124 (×2), 76 (×2), 111 (×2), 58 (×2), 149 (×2), 144 (×2), 87 (×2), 119 (×2), 153 (×2), 142 (×2), 187 (×1), 95 (×1), 81 (×1), 90 (×1), 56 (×1), 94 (×1), 98 (×1), 160 (×1), 203 (×1), 120 (×1), 101 (×1), 97 (×1), 64 (×1)
Sampling rate (Hz)	1000 (×102), 512 (×40), 2000 (×16), 400 (×8), 499.7071 (×6)
Total duration (h)	180.6
Size on disk	97.8 GB
Recording type	IEEG
Experimental modality	Visual
Paradigm type	Memory
Population	Surgery
BIDS version	1.7.0
Source	openneuro
License	CC0
NEMAR citations	0

Tasks

• pyFR

Upstream README

Verbatim from the dataset's authors — the canonical description.

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.

People

Authors

• Haydn G. Herrema
• Michael J. Kahana (senior)

Contact

• Haydn Herrema

Funding

• NIH: MH055687
• NIH: MH061975

Links

• DOI: 10.18112/openneuro.ds004865.v2.0.1
• OpenNeuro: ds004865
• Browse 700+ datasets: EEGDash catalog
• Docs: https://eegdash.org
• Code: https://github.com/eegdash/EEGDash

Provenance

• Backend: s3 — s3://openneuro.org/ds004865
• Exact size: 104,999,471,870 bytes (97.8 GB)
• Ingested: 2026-04-06
• Stats computed: 2026-04-04

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Auto-generated from dataset_summary.csv and the EEGDash API. Do not edit this file by hand — update the upstream source and re-run scripts/push_metadata_stubs.py.