Development strategy of eye movement controlled rehabilitation aid using Electro-oculogram

This paper proposes a strategy to develop an eye movement controlled rehabilitation aid using Electro-oculogram (EOG) to help severely paralyzed persons. Here, acquisition of EOG is done with a designed circuit. From EOG, eye movements in left and right directions are classified using radial basis function (RBF) artificial neural network (ANN). For classification wavelet coefficients are used as signal feature. This offline training of the neural network can be used afterwards to generate real time control signals for the implementation of the EOG controlled rehabilitation aids. The approach and challenges concerned with the same have also been discussed in this paper.