Update README.md

README.md

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 license: apache-2.0
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-# SilentWear  
-## Speech Recognition from EMG Signals
 
-SilentWear is a dataset for vocalized and silent speech recognition from surface electromyography (sEMG) signals, recorded from a wearable neckband.
+# 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:
 
-- Silent speech interfaces  
 - EMG-based speech decoding  
 - Human–machine interaction (HMI)  
-- Wearable bio-signal processing  
 - 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 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
+
+Main folder: `silentwear_emg_hmi_26`
+
+It 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:
+``` 
+  ------------------------------------------------------------------------------
+                    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}
+}
+```
+
+
 
-⚠️ **Status: the dataset will be released after review**
----