---
pretty_name: "Categorized Free Recall: Delayed Free Recall of Word Lists Organized by Semantic Categories"
license: cc0-1.0
tags:
  - ieeg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - visual
  - memory
  - epilepsy
size_categories:
  - n<1K
task_categories:
  - other
---

# Categorized Free Recall: Delayed Free Recall of Word Lists Organized by Semantic Categories

**Dataset ID:** `ds004809`

_Herrema2023_Categorized_Free_Recall_

**Canonical aliases:** `catFR_Categorized_Free_Recall` · `CatFR`

> **At a glance:** IEEG · Visual memory · epilepsy · 252 subjects · 889 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="ds004809", 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 catFR_Categorized_Free_Recall
ds = catFR_Categorized_Free_Recall(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/ds004809")
```


## Dataset metadata

| | |
|---|---|
| **Subjects** | 252 |
| **Recordings** | 889 |
| **Tasks (count)** | 1 |
| **Channels** | 126 (×70), 124 (×30), 108 (×26), 125 (×20), 128 (×19), 139 (×19), 88 (×17), 127 (×16), 120 (×16), 131 (×15), 145 (×15), 148 (×15), 116 (×15), 64 (×14), 196 (×14), 112 (×14), 110 (×13), 142 (×13), 179 (×13), 118 (×12), 155 (×12), 133 (×11), 114 (×11), 121 (×11), 251 (×11), 159 (×11), 90 (×11), 178 (×10), 113 (×10), 186 (×10), 94 (×10), 92 (×10), 158 (×9), 115 (×9), 105 (×9), 152 (×9), 198 (×9), 200 (×8), 183 (×8), 156 (×8), 247 (×8), 104 (×8), 106 (×7), 166 (×7), 122 (×7), 98 (×7), 68 (×7), 212 (×7), 240 (×6), 241 (×6), 100 (×6), 109 (×6), 76 (×6), 78 (×6), 184 (×6), 150 (×6), 154 (×5), 56 (×5), 208 (×5), 165 (×5), 168 (×5), 250 (×5), 224 (×4), 141 (×4), 189 (×4), 164 (×4), 192 (×4), 180 (×4), 97 (×4), 72 (×4), 70 (×4), 89 (×4), 238 (×4), 185 (×4), 173 (×4), 219 (×4), 175 (×4), 134 (×4), 188 (×4), 83 (×3), 160 (×3), 167 (×3), 140 (×3), 209 (×3), 95 (×3), 220 (×3), 130 (×3), 162 (×3), 46 (×3), 60 (×3), 229 (×3), 207 (×3), 123 (×2), 119 (×2), 169 (×2), 203 (×2), 161 (×2), 84 (×2), 177 (×2), 151 (×2), 172 (×2), 93 (×2), 53 (×2), 96 (×2), 132 (×2), 67 (×2), 176 (×2), 193 (×2), 187 (×2), 80 (×1), 146 (×1), 14 (×1), 136 (×1), 52 (×1), 16 (×1), 86 (×1), 239 (×1), 75 (×1), 182 (×1), 102 (×1), 85 (×1), 63 (×1), 206 (×1), 50 (×1), 213 (×1), 111 (×1), 99 (×1), 62 (×1), 37 (×1), 163 (×1), 243 (×1), 36 (×1), 107 (×1), 153 (×1), 143 (×1), 26 (×1), 202 (×1), 218 (×1) |
| **Sampling rate (Hz)** | 1000 (×766), 500 (×93), 1600 (×10), 999 (×8), 1023.999 (×6), 1024 (×4), 499.7071 (×2) |
| **Total duration (h)** | 575.3 |
| **Size on disk** | 477.2 GB |
| **Recording type** | IEEG |
| **Experimental modality** | Visual |
| **Paradigm type** | Memory |
| **Population** | Epilepsy |
| **Source** | openneuro |
| **License** | CC0 |
| **NEMAR citations** | 1.0 |

## Links

- **DOI:** [10.18112/openneuro.ds004809.v2.2.0](https://doi.org/10.18112/openneuro.ds004809.v2.2.0)
- **OpenNeuro:** [ds004809](https://openneuro.org/datasets/ds004809)
- **Browse 700+ datasets:** [EEGDash catalog](https://huggingface.co/spaces/EEGDash/catalog)
- **Docs:** <https://eegdash.org>
- **Code:** <https://github.com/eegdash/EEGDash>

---

_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/ds004809). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._