Upsampling eye movement signal using Convolutional Neural Networks

Abstract

It is common in eye movement acquisition that data comes from different devices and exhibits different sampling rates. One of the solutions to this problem is to recalculate the signal into another sampling rate. When upsampling is performed on a sequence of samples, it approximates the sequence that would have been obtained by sampling the signal at a higher rate. Many methods may be used for eye movement signal upsampling. Recently, Convolutional Neural Networks proved to be very efficient in upsampling (or supersampling) digital images. This paper attempts to utilize Convolutional Neural Networks using two architectures with different types of layers on the signal sampled with 125 Hz to obtain an eight-time increase in the sampling rate and produce the signal with a 1000 Hz sampling rate. The experiments on the GazeBase dataset proved that this solution is feasible, and the Convolutional Neural Network can learn the characteristic of the eye movement signal.