Simulation and Modelling of BCI Based Multi Purpose Wheel Chair for Paralysed People

Abstract

According to the survey of Reeve foundation and WHO says, there are nearly 1 in 50 people living with paralysis, every year 5.4 million people affected. Paralysis is caused by spinal injury, stroke, multiple sclerosis, cerebral palsy and other causes like motor accidents and victim of violence. It is also called loss of muscle function in some part of the body. In this paper, we systematically survey a different techniques used for brain computer interface and also review the research on non-invasive, electroencephalography (EEG)-based BCI systems for communication and rehabilitation. Main research focus on previous techniques like deep learning or deep neural network, machine learning, Neuro plasticity, support vector machine, artifact suppression and so on. In this Differing from traditional machine learning algorithms, online sequential machine learning algorithm is empowered to learn distinct high-level representations from raw brain signals without manual feature selection. A certain MATLAB program is designed to use these motions. Our results are to exploit the accuracy rate and to generate the assistive devices for restoration of movement and communication strength for physically disabled patients in order to rehabilitate their lost motor abilities.