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ds004330
The spatiotemporal neural dynamics of object recognition for natural images and line drawings (MEG)
openneuro
https://openneuro.org/datasets/ds004330
10.18112/openneuro.ds004330.v1.0.0
CC0
{ "library": "eegdash", "class": "EEGDashDataset", "kwargs": { "dataset": "ds004330" } }
https://huggingface.co/spaces/EEGDash/catalog
huggingface-space/scripts/push_metadata_stubs.py

The spatiotemporal neural dynamics of object recognition for natural images and line drawings (MEG)

Dataset ID: ds004330

Singer2022

At a glance: MEG · Visual perception · healthy · 30 subjects · 270 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="ds004330", cache_dir="./cache")
print(len(ds), "recordings")

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/ds004330")

Dataset metadata

Subjects 30
Recordings 270
Tasks (count) 1
Channels 310 (×270)
Sampling rate (Hz) 1000 (×270)
Total duration (h) 36.7
Size on disk 153.7 GB
Recording type MEG
Experimental modality Visual
Paradigm type Perception
Population Healthy
Source openneuro
License CC0
NEMAR citations 1.0

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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