metadata
pretty_name: 'FRL Discrete Gestures: Hand Gesture Recognition from Surface Electromyography'
license: cc-by-nc-4.0
tags:
- eeg
- neuroscience
- eegdash
- brain-computer-interface
- pytorch
size_categories:
- n<1K
task_categories:
- other
FRL Discrete Gestures: Hand Gesture Recognition from Surface Electromyography
Dataset ID: nm000105
Kaifosh2025
Canonical aliases: FRL_DiscreteGestures
At a glance: EMG · 100 subjects · 100 recordings · CC-BY-NC 4.0
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="nm000105", 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 FRL_DiscreteGestures
ds = FRL_DiscreteGestures(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/nm000105")
Dataset metadata
| Subjects | 100 |
| Recordings | 100 |
| Tasks (count) | 1 |
| Channels | 16 (×100) |
| Sampling rate (Hz) | 2000 (×100) |
| Total duration (h) | 63.9 |
| Size on disk | 20.6 GB |
| Recording type | EMG |
| Source | nemar |
| License | CC-BY-NC 4.0 |
Links
- DOI: 10.82901/nemar.nm000105
- NEMAR: nm000105
- 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.