Brain Computer Interface For Paralyzed People

Abstract

A Brain-Computer Interface (BCI) is a communication system designed to allow the users who has suffer of totally paralysis to send messages or commands without sending them through the brains normal output pathway. The overall objective of this project is to design and implement an algorithm that could separate and classify task-related Electroencephalography (EEG) signals which is the movement of right or left index finger from ongoing EEG signals by using the Independent Component Analysis (ICA). This separation would effectively speed the classification of EEG patterns. The task-related EEG signals were taken and classified using adaptive pattern classifier which is consist of combing the Kohonen Self-Organizing Map (SOM) with Learning Vector Quantization (LVQ). The algorithm was trained and tested using offline EEG signals measured according to ten-twenty International system obtained from a computerized EEG system in Ibn-Rushd Hospital.