---
license: apache-2.0
language:
- en
tags:
- silent_speech
- speech
- EMG
- wearable
- neuromotor
- HMI
---

# SilentWear: An Ultra-Low Power Wearable Interface for EMG-Based Silent Speech Recognition

This repository provides a multi-session surface electromyography (EMG) dataset for vocalized and silent speech recognition, recorded using a wearable neckband interface.

The dataset is designed to support research in:

- EMG-based speech decoding  
- Human–machine interaction (HMI)  
- Assistive communication technologies  
- Ultra-low-power wearable AI systems  

The data were collected using **SilentWear**, an unobtrusive, ultra-low-power EMG neckband designed for silent and vocalized speech detection.

![SilentWear Device](./images/abstract_fig_git.png)
![SilentWear Signals](./images/signals.png)

---
# Dataset Description

The dataset includes recordings from:

- **4 subjects** (3 male, 1 female)
- **Vocalized** and **silent** speech conditions
- **8 HMI commands**:  
  *up*, *down*, *left*, *right*, *start*, *stop*, *forward*, *backward*  
  plus a *rest* (no-speech) class
- **3 recording days** per subject  
- **Multiple sessions, collected over 3 days**, each containing:
  - 5 vocalized batches.
  - 5 silent batches  
- Each batch contains *20 repetitions* of each word, plus rest. 

This structure enables evaluation under **multi-day conditions**, supporting research on robustness to electrode repositioning and inter-session variability.

Further details on the data collection methodology are available at:  
https://arxiv.org/placeholder

---
# Repository Organization
The repository contains two subfolders:
### 1️⃣ `data_raw_and_filt`

This folder contains full-length EMG recordings for each subject,
condition, session, and batch.

Each file: 
- Contains raw EMG signals
- Contains filtered EMG signals (4th-order high-pass at 20 Hz + 50 Hz notch)
- Is stored in `.h5` format\
- Uses the HDF5 key `"emg"`
- 
Directory structure example:

```text
data_raw_and_filt/
└── S01/s
    └── silent/
        └── sess_1_batch_1.h5
        .
        .
        └── sess_3_batch_5.h5
    └── vocalized/
        └── sess_1_batch_1.h5
        .
        .
        └── sess_3_batch_5.h5
└── S02
└── S03
└── S04

```

------------------------------------------------------------------------

#### Example: Loading a File

``` python
import pandas as pd

df = pd.read_hdf("data_raw_and_filt/S01/silent/sess_1_batch_1.h5", key="emg")
df.head()
```
------------------------------------------------------------------------

#### File Content Structure (`data_raw_and_filt`)

Each `.h5` file contains:

| Group        | Columns                  | Description                     |
| ------------ | ------------------------ | ------------------------------- |
| Raw EMG      | `Ch_0`–`Ch_15`           | Raw sEMG samples                |
| Filtered EMG | `Ch_0_filt`–`Ch_15_filt` | High-pass (20 Hz) + 50 Hz notch |
| Labels       | `Label_int`, `Label_str` | Integer and string class labels |
| Metadata     | `session_id`, `batch_id` | Session and batch identifiers   |

### 2️⃣ `wins_and_features`
- Non-overlapping windowed segments  
- Raw and filtered signals  
- Extracted time-frequency features  

These files can be directly used for model training or benchmarking.  
---

# Code and Usage

The dataset is designed to be used in conjunction with the SilentWear repository:

https://github.com/pulp-bio/silent_wear

Please refer to the repository `README.md` for:

- Data loading utilities  
- Preprocessing pipelines  
- Training scripts  
- Evaluation scripts 

The repository creates the files contained in `wins_and_features` folder; these files are then used for model training.

Alternatively, you may directly use the `data_raw_and_filt` folder to:

- Build custom dataloaders  
- Train your own architectures  
- Benchmark novel EMG decoding methods  

---

# Contributing

We aim to promote standardized evaluation and fair comparison across models.

We strongly encourage contributions of trained models and evaluation results to:

https://github.com/pulp-bio/silent_wear  

Please refer to the repository README for submission guidelines.

---
# Citation

If you use this dataset, please cite:

```bibtex
@online{spacone_silentwear_26,
  author = {Spacone, Giusy and Frey, Sebastian and Pollo, Giovanni and Burrello, Alessio and Pagliari, J. Daniele and Kartsch, Victor and Cossettini, Andrea and Benini, Luca},
  title = {SilentWear: An Ultra-Low Power Wearable Interface for EMG-Based Silent Speech Recognition},
  year = {2026},
  url = {coming soon}
}
```
## 📄 License

See the `LICENSE` file for the full license text.

This project makes use of the following licenses:

- Apache License 2.0 — See the `LICENSE` file for the full license text.

- Images are under the the Creative Commons Attribution 4.0 International License - see the `LICENSE.images` file for details.


## 👨‍💻 Contributors

_Silent-Wear_ has been developed at _ETH Zürich_, by the [PULP-Bio](https://iis-projects.ee.ethz.ch/index.php?title=Biomedical_Circuits,_Systems,_and_Applications):

- [Giusy Spacone](https://scholar.google.com/citations?user=dGE8uMEAAAAJ&hl=en) (Conceptualization, Experimental Design, Development)
- [Sebastian Frey](https://scholar.google.com/citations?user=7jhiqz4AAAAJ&hl=en) (PCB design, Firmware, Documentation)
- Fiona Meier (Hardware Development)
- [Giovanni Pollo](https://scholar.google.com/citations?hl=it&user=znSV3doAAAAJ&view_op=list_works&sortby=pubdate) (Experimental Desing, Data Collection, Documentation)

- [Prof. Luca Benini](https://scholar.google.com/citations?user=8riq3sYAAAAJ&hl=en)(Supervision, Conceptualization)
- [Dr. Andrea Cossettini](https://scholar.google.com/citations?user=d8O91jIAAAAJ&hl=en)(Supervision, Project administration)