Datasets:

EEGDash
/

ds005558

pretty_name: >-
  Categorized Free Recall with Closed-Loop Stimulation at Encoding (Encoding
  Classifier)
license: cc0-1.0
tags:
  - ieeg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - visual
  - memory
  - surgery
size_categories:
  - n<1K
task_categories:
  - other

Categorized Free Recall with Closed-Loop Stimulation at Encoding (Encoding Classifier)

Dataset ID: ds005558

Herrema2024_Categorized_Free

Canonical aliases: catFR_closed_loop

At a glance: IEEG · Visual memory · surgery · 7 subjects · 22 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="ds005558", 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_closed_loop
ds = catFR_closed_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/ds005558")

Dataset metadata


Subjects	7
Recordings	22
Tasks (count)	1
Channels	126 (×3), 124 (×2), 128 (×2), 113 (×2), 114 (×2), 78 (×2), 68 (×2), 156 (×2), 122 (×1), 90 (×1), 56 (×1), 110 (×1), 64 (×1)
Sampling rate (Hz)	1000 (×22)
Total duration (h)	17.0
Size on disk	12.2 GB
Recording type	IEEG
Experimental modality	Visual
Paradigm type	Memory
Population	Surgery
Source	openneuro
License	CC0
NEMAR citations	0.0

Categorized Free Recall with Closed-Loop Stimulation at Encoding (Encoding Classifier)

Load this dataset

Dataset metadata

Links