🧠 EEG Brain-Computer Interface (BCI) Cursor Control
This project enables users to control their computer cursor using brainwaves. It employs a Random Forest Classifier trained on high-frequency EEG band ratios (Alpha, Beta, Theta, and Gamma) to detect directional intent and "click" actions. Key capabilities include Real-time Signal Processing : Advanced feature engineering including 8+ neuroscience-standard
About this project
🧠 EEG Brain-Computer Interface (BCI) Cursor Control: This project enables users to control their computer cursor using brainwaves. It employs a Random Forest Classifier trained on high-frequency EEG band ratios (Alpha, Beta, Theta, and Gamma) to detect directional intent and "click" actions. Key capabilities include Real-time Signal Processing : Advanced feature engineering including 8+ neuroscience-standard EEG ratios.; Signal Smoothing : 3-point rolling average to eliminate environmental noise.; Dynamic Training UI : Professional data collector with Pause/Resume, adjustable sample rates, and visual feedback.. The implementation uses Python, scikit-learn, Pandas, NumPy.