A Brain Computer Interface–Based P300 Speller for Home Appliances Control System

Abstract

Enviourment control is one of the critical difficulties for handicapped individuals who experience the ill effects of neuromuscular ailments. Brain computer interface systems empower a subject to communicate with a PC machine without drawing down any solid action. This communication does not depend in light of any ordinary medium of correspondences like physical developments, talking, and motion and so forth. The most vital desire for a home control application is high accuracy and solid control. In this study, row column–based (2 Row, 3 columns) P300 paradigm for home appliances control was designed. In this article we analyse a real time EEG data for P300 speller using support vector machine and artificial neural network for high accuracy. Using this proposed method we are able to find the target appliance in a correct and fastest way. Four healthy subjects were participating in this study. Artificial neural network gives 85% accuracy within 10 flashes. The results shows this paradigm can be used for select option of a home appliances control application for healthy subjects with user convenient and reliable.