PCA Based EEG event features extraction for robotics dexterous grasping

Abstract

The presented work is dedicated towards deep understanding of resulting Electroencephalography (EEG) brainwaves during a typical grasp and lift human grasping task. During grasping, forces are applied by fingertips dexterously, as observed through resulting EEG waves. For mirroring this to a dexterous robotic hand, methods have to be developed to find features for optimal forces, movements, and right finger joints displacements. Resulting EEG brainwaves during grasp and lift task are very useful, however these EEG waves are related, correlated, complicated, and raw. With the potential and analysis of Principal Components Analysis (PCA) of EEG, it indicated an overlap of valuable neural behaviors from various locations over the human skull, indicating interrelated and coupled events for robotic grasping. PCA has been used to unlock few main features of EEG waves during a grasp and lift task. The foremost grasping features are hence used in creating events for a robotic dexterous grasping.