🧠 FeelTheWaves: EEG-Based Emotion Recognition Web App

NeuroFeel is a Streamlit-powered web application that classifies emotional states from EEG signal features. Upload a CSV file (like the emotions.csv dataset from Kaggle), train different ML models, visualize performance, and even test real-time predictions from manual input.

🚀 Features

• 📥 Upload EEG feature CSV
• 🧪 Choose between multiple classifiers:
  • Random Forest
  • SVM
  • K-Nearest Neighbors
  • Logistic Regression
• 🔁 5-Fold Cross-Validation
• 📊 Label Distribution Pie Chart
• ✅ Model Evaluation (Accuracy + Classification Report)
• 🔢 Confusion Matrix Heatmap
• 🔬 Feature Importance for Tree Models
• 🔎 Predict a Single Row
• 🎛️ Real-Time Input for Manual Predictions
• 💾 Download trained model (.pkl) and predictions (.csv)

📁 Folder Structure

NeuroFeel/
├── app.py                  # Main Streamlit app
├── requirements.txt        # Python dependencies
├── README.md               # Project info

⚙️ Installation

Locally

git clone https://github.com/yourusername/NeuroFeel.git
cd NeuroFeel
pip install -r requirements.txt
streamlit run app.py

In Google Colab (Minimal version)

!pip install streamlit pandas scikit-learn matplotlib seaborn joblib

📊 Dataset Format

Ensure your CSV file:

• Has numerical EEG features
• Includes a label column for emotion names

Example:

alpha,beta,theta,gamma,label
0.1,0.2,0.3,0.4,happy
...

📦 Dependencies

pandas
numpy
scikit-learn
matplotlib
seaborn
joblib
streamlit

📌 To-Do

• Add deep learning model options (LSTM/MLP)
• Add multi-model comparison feature
• Add time-series EEG support

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💡 Inspiration

This project is perfect for neuroscience, AI + mental health research, or as a student portfolio project exploring the intersection of brain signals and emotions.

🧠 Made with ❤️ by [Your Name]