Datasets:

EEGDash
/

ds004395

pretty_name: Penn Electrophysiology of Encoding and Retrieval Study (PEERS)
license: cc0-1.0
tags:
  - eeg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - visual
  - memory
size_categories:
  - 1K<n<10K
task_categories:
  - other

Penn Electrophysiology of Encoding and Retrieval Study (PEERS)

Dataset ID: ds004395

Kahana2023

Canonical aliases: PEERS

At a glance: EEG · Visual memory · healthy · 364 subjects · 6483 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="ds004395", 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 PEERS
ds = PEERS(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/ds004395")

Dataset metadata


Subjects	364
Recordings	6483
Tasks (count)	3
Channels	129 (×4980), 137 (×1490), 144 (×11), 272 (×2)
Sampling rate (Hz)	500 (×4946), 2048 (×1466), 512 (×28), 250 (×17), 1000 (×15), 1024 (×11)
Total duration (h)	9,115.8
Size on disk	8.7 TB
Recording type	EEG
Experimental modality	Visual
Paradigm type	Memory
Population	Healthy
Source	openneuro
License	CC0
NEMAR citations	6.0

Penn Electrophysiology of Encoding and Retrieval Study (PEERS)

Load this dataset

Dataset metadata

Links