A cost function to determine the optimum filter andparameters for stabilising gaze data

摘要

Prior to delivery of data, eye tracker software may apply filtering to correct for noise. Although filtering produces much better precision of data, it may add to the time it takes forthe reporting of gaze data to stabilise after a saccade due tothe usage of a sliding window. The effect of various filters andparameter settings on accuracy, precision and filter related latency is examined. A cost function can be used to obtain theoptimal parameters (filter, length of window, metric and threshold for removal of samples and removal percentage). It was found that for any of the FIR filters, the standard deviation of samples can be used to remove 95% of samples in the window so than an optimum combination of filter related latency and precision can be obtained. It was also confirmed that for unfiltered data, the shape of noise, signified byRMS/STD, is around2as expected for white noise, whereas lowerRMS/STD values were observed for allfilters