EMBEDDED COMPUTATIONAL INTELLIGENCE BASED DEVELOPMENT OF BRAIN

Abstract

The sever accidents, paralysis attacks and different diseases are major cause of interrupting the normal communication channel of brain with other body parts. The individuals, victim of above sever cases can’t live a normal life and are burden on the society. To help these affected people who may have partially or completely lost independent motions of their limbs, there is requirement of a system which can bypass the normal communication channel of the brain and sends messages to the exterior world. To implement such kind of system, acquiring, filtering, feature extraction and classifying the brain signals is a major task. The focus of this research is to classify the EEG signals dataset by Artificial Neural Network, Support Vector Machine and well-known statistical techniques e.g. Linear Discriminant Analysis, Quadratic Discriminant Analysis, Naive Bayes and Decision Trees and also compare them to identify a suitable technique for hardware implementation. The performances of these classifiers are compared on the basis of confusion matrix and mean square error. The most efficient method will be used to give signals to microcontroller to control the motion of 2-DoF Robotic Manipulator for Upper Limb Prosthesis. The 2-DoF Robotic Manipulator designed in NUST will be used for testing. This research enhances the future potential capabilities of BCI systems.