A BCI System for Physically Challenged Based on Alpha Dynamics of EEG Signals

Abstract Millions of people around the world suffer from paralysis. People having paralysis need new devices with sophisticated technologies to help them for comfortable live. Most existing brain computer interfaces (BCIs) are designed to control a wheelchair using eye movement, joy stick and chin movement have a many limitation and inconvenient to use. In this study, a brain computer interface based on alpha dynamics of EEG has been attempted to provide an alternative channel for severely disabled for communicating with the external world. In the present study, people who are not able to move their body part can also control the wheelchair just by thought process Wheelchair EEG data was mapped in alpha frequency band. Wavelet packet transform was used for feature extraction. Performance of three classifiers was compared for classification of five imagery tasks. Radial basis neural network was observed most suitable for classification EEG signal. EEG based offline system for wheelchair control has been designed and implemented on laboratory developed prototype.