EEGDash/ds004809

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 streams it on demand and returns a PyTorch / braindecode dataset.

# 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:

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:

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
• OpenNeuro: ds004809
• Browse 700+ datasets: EEGDash catalog
• Docs: https://eegdash.org
• Code: https://github.com/eegdash/EEGDash

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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.