metadata
pretty_name: TNO
license: cc0-1.0
tags:
- eeg
- neuroscience
- eegdash
- brain-computer-interface
- pytorch
size_categories:
- n<1K
task_categories:
- other
TNO
Dataset ID: ds004660
Johnson2023_TNO
Canonical aliases: TNO
At a glance: EEG · 21 subjects · 42 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="ds004660", 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 TNO
ds = TNO(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/ds004660")
Dataset metadata
| Subjects | 21 |
| Recordings | 42 |
| Tasks (count) | 1 |
| Channels | 38 (×42) |
| Sampling rate (Hz) | 512 (×41), 2048 (×1) |
| Total duration (h) | 23.8 |
| Size on disk | 7.2 GB |
| Recording type | EEG |
| Source | openneuro |
| License | CC0 |
| NEMAR citations | 1.0 |
Links
- DOI: 10.18112/openneuro.ds004660.v1.0.2
- OpenNeuro: ds004660
- Browse 700+ datasets: EEGDash catalog
- Docs: https://eegdash.org
- Code: https://github.com/eegdash/EEGDash
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.