EEGDash/nm000104

---
pretty_name: "emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography"
license: cc-by-nc-sa-4.0
tags:
- eeg
- neuroscience
- eegdash
- brain-computer-interface
- pytorch
size_categories:
- 1K<n<10K
task_categories:
- other
---

# emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography

**Dataset ID:** `nm000104`

_Sivakumar2024_

**Canonical aliases:** `emg2qwerty`

> **At a glance:** EMG · 108 subjects · 1136 recordings · CC-BY-NC-SA-4.0

## Load this dataset

This repo is a **pointer**. The raw EEG data lives at its canonical source
(OpenNeuro / NEMAR); [EEGDash](https://github.com/eegdash/EEGDash) streams it
on demand and returns a PyTorch / braindecode dataset.

```python
# pip install eegdash
from eegdash import EEGDashDataset

ds = EEGDashDataset(dataset="nm000104", cache_dir="./cache")
print(len(ds), "recordings")
```

You can also load it by canonical alias — these are registered classes in `eegdash.dataset`:

```python
from eegdash.dataset import emg2qwerty
ds = emg2qwerty(cache_dir="./cache")
```

If the dataset has been mirrored to the HF Hub in braindecode's Zarr layout,
you can also pull it directly:

```python
from braindecode.datasets import BaseConcatDataset
ds = BaseConcatDataset.pull_from_hub("EEGDash/nm000104")
```

## Dataset metadata

| | |
|---|---|
| **Subjects** | 108 |
| **Recordings** | 1136 |
| **Tasks (count)** | 1 |
| **Channels** | 32 (×1136) |
| **Sampling rate (Hz)** | 2000 (×1136) |
| **Total duration (h)** | 346.3 |
| **Size on disk** | 223.3 GB |
| **Recording type** | EMG |
| **Source** | nemar |
| **License** | CC-BY-NC-SA-4.0 |

## Links

- **DOI:** [10.82901/nemar.nm000104](https://doi.org/10.82901/nemar.nm000104)
- **NEMAR:** [nm000104](https://nemar.org/dataexplorer/detail?dataset_id=nm000104)
- **Browse 700+ datasets:** [EEGDash catalog](https://huggingface.co/spaces/EEGDash/catalog)
- **Docs:** <https://eegdash.org>
- **Code:** <https://github.com/eegdash/EEGDash>

---

_Auto-generated from [dataset_summary.csv](https://github.com/eegdash/EEGDash/blob/main/eegdash/dataset/dataset_summary.csv) and the [EEGDash API](https://data.eegdash.org/api/eegdash/datasets/summary/nm000104). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._