Journal du génie chimique et de la technologie des procédés
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Random forest based electroencephalography classification for robotics dexterous hands movement

Ebrahim A Mattar

This research work is focusing on the applications of AI random forest based Electroencephalography (EEG) classification for robotics dexterous hands movements, for interpretation and understanding of the brainwaves resulting from electroencephalography during a human grasping task. The algorithm has been designed in such a way to allow an understanding and making use of how human is thinking during grasping and fingers movement’s events. These thinking patterns are then used to create an intelligent behavior for a robotic hand and fingers movements. The research is novel in a sense; it relies on detecting grasping features for a human grasping using Principle Component Analysis (PAC) or even (ICA), hence to learn these features for robotics applications.