Majed Eldeen Saydeh

EEG Signal Acquisition and Classification System Developed a complete EEG signal acquisition system using a microcontroller and PC, focusing on signal fidelity and real-time processing. Designed and implemented algorithms in MATLAB for real-time EEG pattern recognition and behavioral state classification using machine learning, achieving a classification accuracy of over 90%. Built an EEG-based Brain-Machine Interface (BMI), allowing users to control external devices via brain signals. Key Accomplishments: Successfully developed a cost-effective EEG system with high fidelity and precision. Integrated machine learning for real-time signal processing and classification with over 90% accuracy.