EEG-Based-Seizure-Prediction

Overview

An advanced real-time EEG (Electroencephalogram) signal processing and seizure detection system using machine learning techniques and interactive visualization. This system processes EEG signals, extracts relevant features, and utilizes multiple machine learning models to predict seizure events in real-time.

Features

• Real-time EEG Processing

  • Artifact removal and signal cleaning
  • Advanced feature extraction
  • Time and frequency domain analysis
  • Wavelet transformation

• Multiple Machine Learning Models

  • Random Forest Classifier
  • Support Vector Machine
  • Gradient Boosting
  • Neural Networks (CNN, LSTM)
  • Ensemble Model

• Interactive Dashboard

  • Real-time EEG signal visualization
  • Live seizure probability monitoring
  • Frequency spectrum analysis
  • Model performance comparison
  • Interactive start/stop controls

Technical Features

• Time-domain feature extraction (mean, variance, skewness, etc.)
• Frequency-domain analysis (power spectral density, frequency bands)
• Wavelet transformation for time-frequency analysis
• Artifact detection and removal
• Multiple classification algorithms
• Real-time data processing and visualization

Dashboard Features

1. Live EEG Signal Display

   • Real-time signal visualization
   • Artifact marking
   • Signal quality indicators

2. Seizure Prediction

   • Real-time probability estimation
   • Multiple model predictions
   • Threshold-based alerts

3. Frequency Analysis

   • Power spectrum visualization
   • Frequency band distribution
   • Real-time spectral updates

4. Model Performance

   • Comparison of different models
   • Prediction confidence scores
   • Performance metrics

Models and Methods

• Feature Extraction

  • Statistical features (mean, variance, skewness, kurtosis)
  • Time-domain features (zero crossings, line length)
  • Frequency-domain features (band powers, spectral ratios)
  • Wavelet coefficients

• Machine Learning Models

  • Random Forest: Robust ensemble classifier
  • SVM: Non-linear pattern recognition
  • Gradient Boosting: Sequential ensemble learning
  • Neural Networks: Deep learning approach
  • Ensemble: Combined model predictions

Future Enhancements

• Integration with real EEG hardware
• Additional machine learning models
• Enhanced visualization features
• Mobile device support
• Cloud-based processing options

Author

Shadrack Addo

Acknowledgments

• Scientific literature on EEG processing
• Open-source machine learning community
• Data visualization libraries