metadata
pretty_name: 'HySER: High-Density Surface Electromyogram Recordings'
license: other
tags:
- eeg
- neuroscience
- eegdash
- brain-computer-interface
- pytorch
size_categories:
- 1K<n<10K
task_categories:
- other
HySER: High-Density Surface Electromyogram Recordings
Dataset ID: nm000108
Jiang2021
Canonical aliases: HySER · Hyser
At a glance: EMG · 20 subjects · 1514 recordings · ODC-By-1.0
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="nm000108", 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 HySER
ds = HySER(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/nm000108")
Dataset metadata
| Subjects | 20 |
| Recordings | 1514 |
| Tasks (count) | 38 |
| Channels | 256 (×1514) |
| Size on disk | 108.2 GB |
| Recording type | EMG |
| Source | nemar |
| License | ODC-By-1.0 |
Links
- DOI: 10.82901/nemar.nm000108
- NEMAR: nm000108
- 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.