Human-Machine Interface-Based Robotic Wheel Chair Control
This chapter presents the P300-based human machine interference (HMI) systems control robotic wheel chair (RWC) prototype in right, left, forward, backward, and stop positions. Four different targets letters are used to elicit the P300 waves, flickering in the low frequency region, by using oddball paradigms and displayed on a liquid crystal display (LCD) screen by Lab-VIEW. After the pre-processing and taking one second time window, feature is extracted by using discrete wavelet transform (DWT). Three different classifiers—two based on ANNs pattern recognition neural network (PRNN) and feed forward neural network (FFNN) and the and other one based on support vector machine (SVM)—are used. Those three techniques are designed and compared with the different accuracies among them.