EEGDash/ds005059

Paired Associates Learning: Memory for Word Pairs in Cued Recall

Dataset ID: ds005059

Herrema2024_Paired

Canonical aliases: PAL

At a glance: IEEG · Visual memory · epilepsy · 69 subjects · 282 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="ds005059", 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 PAL
ds = PAL(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/ds005059")

Dataset metadata

Subjects	69
Recordings	282
Tasks (count)	1
Channels	112 (×22), 126 (×15), 85 (×11), 110 (×10), 128 (×10), 104 (×9), 88 (×9), 100 (×9), 72 (×8), 64 (×8), 186 (×8), 102 (×7), 116 (×7), 121 (×7), 92 (×6), 142 (×6), 119 (×5), 97 (×5), 95 (×5), 94 (×5), 106 (×4), 140 (×4), 124 (×4), 96 (×4), 123 (×4), 139 (×4), 86 (×4), 130 (×4), 68 (×4), 87 (×3), 107 (×3), 188 (×3), 84 (×3), 120 (×3), 58 (×3), 74 (×3), 114 (×3), 83 (×3), 108 (×3), 55 (×3), 80 (×3), 117 (×3), 173 (×3), 118 (×2), 141 (×2), 73 (×2), 138 (×2), 115 (×2), 122 (×2), 111 (×2), 149 (×2), 60 (×1), 146 (×1), 77 (×1), 67 (×1), 93 (×1), 76 (×1), 46 (×1), 53 (×1), 14 (×1), 99 (×1), 177 (×1), 90 (×1), 98 (×1), 52 (×1), 133 (×1), 16 (×1)
Sampling rate (Hz)	1000 (×193), 500 (×71), 1024 (×8), 499.7071 (×6), 1600 (×4)
Total duration (h)	261.3
Size on disk	167.3 GB
Recording type	IEEG
Experimental modality	Visual
Paradigm type	Memory
Population	Epilepsy
Source	openneuro
License	CC0
NEMAR citations	0.0

Links

• DOI: 10.18112/openneuro.ds005059.v1.0.6
• OpenNeuro: ds005059
• 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.