Datasets:

EEGDash
/

ds005491

pretty_name: Categorized Free Recall with Open-Loop Stimulation at Encoding
license: cc0-1.0
tags:
  - ieeg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - visual
  - clinical-intervention
size_categories:
  - n<1K
task_categories:
  - other

Categorized Free Recall with Open-Loop Stimulation at Encoding

Dataset ID: ds005491

Herrema2024_Categorized

Canonical aliases: catFR_open_loop · RAM_catFR · catFR_stim

At a glance: IEEG · Visual clinical/intervention · unknown · 19 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="ds005491", 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_open_loop
ds = catFR_open_loop(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/ds005491")

Dataset metadata


Subjects	19
Recordings	51
Tasks (count)	1
Channels	64 (×5), 126 (×5), 88 (×4), 85 (×3), 93 (×3), 113 (×2), 163 (×2), 104 (×2), 155 (×2), 133 (×2), 116 (×2), 119 (×1), 72 (×1), 68 (×1), 92 (×1), 96 (×1), 127 (×1), 177 (×1), 146 (×1), 78 (×1), 128 (×1), 124 (×1), 110 (×1), 70 (×1), 80 (×1), 14 (×1), 115 (×1), 130 (×1), 112 (×1), 16 (×1)
Sampling rate (Hz)	500 (×39), 1600 (×6), 999 (×4), 1000 (×2)
Total duration (h)	46.7
Size on disk	22.5 GB
Recording type	IEEG
Experimental modality	Visual
Paradigm type	Clinical/Intervention
Population	Unknown
Source	openneuro
License	CC0
NEMAR citations	0.0

Categorized Free Recall with Open-Loop Stimulation at Encoding

Load this dataset

Dataset metadata

Links