Update README.md

README.md

@@ -25,18 +25,160 @@ pipeline_tag: feature-extraction
 library_name: tf
 ---
 
-# bai-64 Mind (Preview) (TR)
+# bai-64 Mind | EEG-to-Text Model [BETA]🧠✍️
 
-#### bai-64 Mind modeli, aklınızdaki kelimeleri ve sayıları dijital ortama aktarabilmek üzere eğitilen derin öğrenme modelidir. EEG üzerinde çalışan bu model heyecan vermekle kalmıyor aynı zamanda da dünyanın en büyüğü ve en güçlüsü olma niteliği taşıyor!
+Classify imagined speech commands from EEG brain signals using deep learning.
 
-#### Keyifli düşünceler...
+![Python](https://img.shields.io/badge/Python-3.10+-blue.svg)
+![TensorFlow](https://img.shields.io/badge/TensorFlow-2.x-orange.svg)
+![License](https://img.shields.io/badge/License-CC_BY_NC_SA_4.0-green)
 
-## --------------
+## Overview
 
-# bai-64 Mind (Preview) (EN)
+This project enables Brain-Computer Interface (BCI) applications by decoding imagined directional commands ("Up", "Down", "Left", "Right") from EEG brain signals. Users think about a direction without speaking, and the system predicts their intended command.
 
-#### bai-64 Mind is a deep learning model trained to digitize words and numbers in your mind. This model, which works on EEG, is not only exciting but also the largest and most powerful in the world!
+## Quick Start
 
-#### Enjoy your thoughts...
+### Installation
 
-**BETA model, 24 Ağustos'ta yayında! / BETA model, available on 24 August!**
+```bash
+pip install -r requirements.txt
+```
+
+### Basic Usage
+
+```python
+import numpy as np
+from tensorflow import keras
+
+# Load pre-trained model
+model = keras.models.load_model('path/to/your/model.h5')
+
+# Your EEG data (1 second, 64 channels, 250 Hz sampling)
+eeg_data = np.random.randn(250, 64)  # Replace with real EEG
+
+# Make prediction
+prediction = model.predict(eeg_data.reshape(1, 250, 64))
+classes = ['Up', 'Down', 'Left', 'Right']
+predicted_command = classes[np.argmax(prediction)]
+
+print(f"Predicted command: {predicted_command}")
+print(f"Confidence: {np.max(prediction):.3f}")
+```
+
+## Real-Time BCI Application
+
+```python
+from analysis import InnerSpeechAnalyzer
+
+# Initialize predictor
+analyzer = InnerSpeechAnalyzer('path/to/your/model.h5')
+predictor = analyzer.create_real_time_predictor()
+
+# Real-time loop
+while True:
+    eeg_data = capture_eeg_signal()  # Your EEG acquisition function
+    command, confidence = predictor.predict_thought(eeg_data)
+    
+    if confidence > 0.8:
+        execute_command(command)  # Your command execution
+        print(f"Executing: {command}")
+```
+
+## Hardware Requirements
+
+### EEG Device
+- **Channels**: 64 Channels (10-20 system)
+- **Sampling Rate**: 250+ Hz
+- **Impedance**: <5kΩ
+- **Bandwidth**: 0.5-100 Hz
+
+### Recommended Devices
+- OpenBCI Cyton + Daisy (16+ channels) (64 channels recommended)
+- Emotiv EPOC X (14 channels) (64 channels recommended)
+- g.tec g.USBamp (Professional) (64 channels recommended)
+
+## Applications
+
+- 🦽 **Assistive Technology**: Control for paralyzed patients
+- 🎮 **Gaming**: Mind-controlled games and VR
+- 🤖 **Robotics**: Brain-controlled robot navigation
+- 💻 **Silent Computing**: Hands-free computer control
+- 🧪 **Research**: Neuroscience and BCI studies
+
+## Data Format
+
+Your EEG data should be:
+- **Shape**: (250, 64) per trial
+- **Duration**: 1 second recording
+- **Channels**: 64 EEG electrodes
+- **Sampling**: 250 Hz
+- **Classes**: ["Up", "Down", "Left", "Right"]
+
+## Features
+
+✅ **Ready-to-use** pre-trained model  
+✅ **Real-time prediction** for BCI applications  
+✅ **Custom training** with your own EEG data  
+✅ **Multiple architectures** (CNN-LSTM, Transformer)  
+✅ **EEG preprocessing** pipeline included  
+✅ **Cross-platform** support (Windows, macOS, Linux)  
+
+## Dependencies
+
+```bash
+tensorflow>=2.8.0,<3.0.0
+scikit-learn>=1.0.0
+numpy>=1.21.0
+scipy>=1.7.0
+pandas>=1.3.0
+mne>=1.0.0
+matplotlib>=3.5.0
+seaborn>=0.11.0
+```
+
+## Example Use Cases
+
+### Wheelchair Control
+```python
+# User thinks "forward" → wheelchair moves forward
+# User thinks "left" → wheelchair turns left
+```
+
+### Smart Home
+```python
+# User thinks "up" → lights turn on
+# User thinks "down" → lights turn off
+```
+
+### Gaming
+```python
+# User thinks "right" → character moves right
+# Mental commands for game control
+```
+
+## Support
+
+- **Web Site**: [Neurazum](https://neurazum.com)
+- **Email**: [contact@neurazum.com](mailto:contact@neurazum.com)
+
+## Note
+
+**This project is in the *BETA* phase. Use at your own risk. Due to the process, low accuracy rates may be observed. In addition, since the data belongs to <span style="color: #ff8d26; "><b>Neurazum</b></span>, the function structure may change in future models.**
+
+## License
+
+CC-BY-NC-SA 4.0 - see [LICENSE](https://creativecommons.org/licenses/by-nc-sa/4.0/) file for details.
+
+### Acknowledgments
+
+1. Neurazum's own data set was used. This data set is closed source.
+2. Nieto, N., Peterson, V., Rufiner, H. L., Kamienkowski, J. E., & Spies, R. (2021).
+"Thinking out loud, an open access EEG-based BCI dataset for inner speech recognition."
+bioRxiv. https://doi.org/10.1101/2021.04.19.440473
+
+---
+
+*Enable mind-controlled technology with EEG! 🚀*
+
+<span style="color: #ff8d26; "><b>Neurazum</b> AI Department</span>