README.md

metadata

license: apache-2.0

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.

---

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:

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

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Example: Loading a File

import pandas as pd

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

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File Content Structure (data_raw_and_filt)

Each .h5 file contains: ```

                Columns                       Description

---

Raw EMG Ch_0--Ch_15 Raw data

Filtered EMG Ch_0_filt--Ch_15_filt High-pass + notch filtered data

Labels Label_int, Integer Labels Label_str String Labels

Session Metadata session_id Recording session identifier

Batch Metadata batch_id Batch identifier within session

### 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 = {202},
  url = {https://arxiv.org/placeholder}
}