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ds004278
Sustained Neural Representations of Personally Familiar People and Places During Cued Recall
openneuro
https://openneuro.org/datasets/ds004278
10.18112/openneuro.ds004278.v1.0.1
CC0
{ "library": "eegdash", "class": "EEGDashDataset", "kwargs": { "dataset": "ds004278" } }
https://huggingface.co/spaces/EEGDash/catalog
huggingface-space/scripts/push_metadata_stubs.py

Sustained Neural Representations of Personally Familiar People and Places During Cued Recall

Dataset ID: ds004278

Kidder2022

Canonical aliases: Kidder2024

At a glance: MEG · Unknown memory · healthy · 30 subjects · 30 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="ds004278", 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 Kidder2024
ds = Kidder2024(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/ds004278")

Dataset metadata

Subjects 30
Recordings 30
Tasks (count) 1
Channels 306 (×30)
Sampling rate (Hz) 1200 (×30)
Total duration (h) 15.5
Size on disk 76.7 GB
Recording type MEG
Experimental modality Unknown
Paradigm type Memory
Population Healthy
Source openneuro
License CC0
NEMAR citations 0.0

Links


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.

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