Update README.md

README.md

@@ -1,138 +1,179 @@
-
 ---
 license: apache-2.0
-language:
-  - en
-tags:
-  - biosignals
-  - emg
-  - silent-speech
-  - speech-recognition
-  - human-machine-interaction
-  - wearable
-  - time-series
-task_categories:
-  - audio-classification
-  - text-classification
-  - signal-processing
-pretty_name: SilentWear EMG Dataset
-dataset_type: other
 ---
 
-# SilentWear: An Ultra-Low-Power Wearable Interface for EMG-Based Silent Speech Recognition
+# 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:
 
-This repository provides a multi-session surface electromyography (sEMG) dataset for vocalized and silent speech recognition, recorded using a wearable neckband interface.
+- EMG-based speech decoding  
+- Human–machine interaction (HMI)  
+- Assistive communication technologies  
+- Ultra-low-power wearable AI systems  
 
-The dataset supports 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.
+
+<p align="center" style="white-space: nowrap;">
+  <img src="images/silent_wear_interface.png"
+       alt="SilentWear Device"
+       style="height:300px; display:inline-block; vertical-align:middle;" />
+  <img src="images/signals.png"
+       alt="SilentWear Signals"
+       style="height:300px; display:inline-block; vertical-align:middle;" />
+</p>
 
 ---
 
-## Dataset Summary
+# Dataset Description
+
+The dataset includes recordings from:
 
-- Subjects: 4 (3 male, 1 female)
-- Conditions: vocalized and silent
-- Commands (8): up, down, left, right, start, stop, forward, backward
-- Additional class: rest (no speech)
-- Days: 3 recording days per subject
-- Sessions:
-  - 5 vocalized batches per session
-  - 5 silent batches per session
-  - 20 repetitions per word per batch
+- **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 multi-day robustness evaluation (e.g., electrode repositioning and session variability).
+This structure enables evaluation under **multi-day conditions**, supporting research on robustness to electrode repositioning and inter-session variability.
 
-Paper link (replace with final link if needed):
+Further details on the data collection methodology are available at:  
 https://arxiv.org/placeholder
 
 ---
 
-## Repository Structure
+# Repository Organization
 
-### 1) data_raw_and_filt
 
-Full-length EMG recordings stored as HDF5 (.h5) files using key "emg".
+The repository contains two subfolders:
+### 1️⃣ `data_raw_and_filt`
 
-Each file contains:
+This folder contains full-length EMG recordings for each subject,
+condition, session, and batch.
 
-- Raw EMG: Ch_0 – Ch_15
-- Filtered EMG: Ch_0_filt – Ch_15_filt (4th-order high-pass @ 20 Hz + 50 Hz notch)
-- Labels: Label_int, Label_str
-- Session metadata: session_id
-- Batch metadata: batch_id
+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"`
 
-Example:
+Directory structure example:
 
+```text
 data_raw_and_filt/
-└── S01/
-    ├── silent/
-    │   ├── sess_1_batch_1.h5
-    │   └── ...
+└── S01/s
+    └── silent/
+        └── sess_1_batch_1.h5
+        .
+        .
+        └── sess_3_batch_5.h5
     └── vocalized/
-        ├── sess_1_batch_1.h5
-        └── ...
-└── S02/
-└── S03/
-└── S04/
+        └── sess_1_batch_1.h5
+        .
+        .
+        └── sess_3_batch_5.h5
+└── S02
+└── S03
+└── S04
+
+```
 
-Example loading:
+------------------------------------------------------------------------
 
+#### 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")
-print(df.head())
+df.head()
+```
 
----
+------------------------------------------------------------------------
+
+#### 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
+                        
 
-### 2) wins_and_features
+  Labels           `Label_int`,                   Integer Labels
+                   `Label_str`                    String Labels
 
-Contains:
-- Non-overlapping windowed segments
-- Raw and filtered signals
-- Extracted time-frequency features
+  Session Metadata `session_id`                   Recording session identifier
 
-These files can be directly used for model training and benchmarking.
+  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.
 
 ---
 
-## Related Code
+# Code and Usage
+
+The dataset is designed to be used in conjunction with the SilentWear repository:
 
-Main repository:
 https://github.com/pulp-bio/silent_wear
 
-Includes:
-- Data loaders
-- Preprocessing pipelines
-- Training scripts
-- Evaluation scripts
+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.
 
-## Intended Use
+Alternatively, you may directly use the `data_raw_and_filt` folder to:
 
-- Silent speech command classification
-- Cross-session robustness studies
-- Low-power wearable EMG decoding research
+- Build custom dataloaders  
+- Train your own architectures  
+- Benchmark novel EMG decoding methods  
 
 ---
 
-## Limitations
 
-- Small number of subjects (n=4)
-- Single sensing configuration (neckband)
-- Fixed vocabulary command set
-- Cross-user generalization may be limited
+# 
+
+# 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
+# Citation
+
+If you use this dataset, please cite:
 
-@online{spacone_silentwear,
+```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    = {https://arxiv.org/placeholder}
+  title = {SilentWear: An Ultra-Low Power Wearable Interface for EMG-Based Silent Speech Recognition},
+  year = {202},
+  url = {https://arxiv.org/placeholder}
 }
+```