---
pretty_name: "pyFR: Delayed Free Recall of Word Lists, Preliminary Cognitive Electrophysiology Study"
license: cc0-1.0
tags:
  - ieeg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - visual
  - memory
  - surgery
  - pyfr
size_categories:
  - n<1K
task_categories:
  - other
authors:
  - "Haydn G. Herrema"
  - "Michael J. Kahana"
---

# 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](https://github.com/eegdash/EEGDash) streams it
on demand and returns a PyTorch / braindecode dataset.

```python
# 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`:

```python
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:

```python
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](https://openneuro.org/datasets/ds004789) & [CatFR1](https://openneuro.org/datasets/ds004809)
#### 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](https://doi.org/10.18112/openneuro.ds004865.v2.0.1)
- **OpenNeuro:** [ds004865](https://openneuro.org/datasets/ds004865)
- **Browse 700+ datasets:** [EEGDash catalog](https://huggingface.co/spaces/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

---

_Auto-generated from [dataset_summary.csv](https://github.com/eegdash/EEGDash/blob/main/eegdash/dataset/dataset_summary.csv) and the [EEGDash API](https://data.eegdash.org/api/eegdash/datasets/summary/ds004865). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._