Datasets:

EEGDash
/

ds005494

pretty_name: >-
  Cued Recall of Paired Associates with Open-Loop Stimulation at Encoding or
  Retrieval
license: cc0-1.0
tags:
  - ieeg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - visual
  - clinical-intervention
size_categories:
  - n<1K
task_categories:
  - other

Cued Recall of Paired Associates with Open-Loop Stimulation at Encoding or Retrieval

Dataset ID: ds005494

Herrema2024_Cued

Canonical aliases: Herrema2024

At a glance: IEEG · Visual clinical/intervention · unknown · 20 subjects · 51 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="ds005494", 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 Herrema2024
ds = Herrema2024(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/ds005494")

Dataset metadata


Subjects	20
Recordings	51
Tasks (count)	1
Channels	100 (×4), 88 (×4), 68 (×3), 128 (×3), 177 (×3), 72 (×3), 141 (×2), 112 (×2), 64 (×2), 114 (×2), 14 (×2), 85 (×2), 16 (×2), 84 (×1), 111 (×1), 93 (×1), 122 (×1), 124 (×1), 95 (×1), 107 (×1), 102 (×1), 86 (×1), 110 (×1), 96 (×1), 146 (×1), 104 (×1), 119 (×1), 121 (×1), 138 (×1), 106 (×1)
Sampling rate (Hz)	500 (×35), 1000 (×16)
Total duration (h)	55.1
Size on disk	26.3 GB
Recording type	IEEG
Experimental modality	Visual
Paradigm type	Clinical/Intervention
Population	Unknown
Source	openneuro
License	CC0
NEMAR citations	0.0

Cued Recall of Paired Associates with Open-Loop Stimulation at Encoding or Retrieval

Load this dataset

Dataset metadata

Links