Jaw-operated human computer interface based on EEG signals via artificial neural networks

Abstract

Man and machine interfaces help paralyzed peoples to communicate and control their environment. This work purpose to introduce and shows a novel kind of machine interface using the horizontal signs of conscious jaw motions on brain signals stored in electroencephalogram (EEG). Electrical functions of the brain are extricated and transformed to control commands. Jaw-Machine Interface (JMI) serve a new functionality for tetraparesis to run peripheral devices with the help of a computer using only horizontal jaw motions. In this study, mean absolute deviation (MAD) and entropy (S) values are derived of EEG and hemispherical designs are valued and examined as offline analysis approach. Principle component analysis (PCA) is used to reduce redundant information from data and two types of artificial neural networks which are Multilayer Neural Network with Levenberg Marquardt training algorithm (MLNN+LM) and Probabilistic Neural Network (PNN) via k-fold method are run to find out horizontal jaw patterns on brain waves.