Automated eye tracking system calibration using artificial neural networks

Abstract

The electro-oculogram (EOG) continues to be widely used to record eye movements especially in clinical settings. However, an efficient and accurate means of converting these recordings into eye position is lacking. An artificial neural network (ANN) that maps two-dimensional (2D) eye movement recordings into 2D eye positions can enhance the utility of such recordings. Multi-layer perceptrons (MLPs) with non-linear activation functions and trained with back propagation proved to be capable of calibrating simulated EOG data to a mean accuracy of 0.33°. Linear perceptrons (LPs) were only nearly half as accurate. For five subjects, the mean accuracy provided by the MLPs was 1.09° of visual angle (°) for EOG data, and 0.98° for an infrared eye tracker. MLPs enabled calibration of 2D saccadic EOG to an accuracy not significantly different from that obtained with the infrared tracker. Using initial weights trained on another person reduced MLP training time, reaching convergence in as little as 20 iterations.