Analysis of Heuristic and Digital Filters as Applied to Video-oculography Signals

TL;DR

This paper compares heuristic and digital filters for video-oculography signals, demonstrating digital filters with a 100 Hz cutoff outperform heuristic filters by better removing high-frequency noise, and recommends digital filtering for offline data processing.

Contribution

The study introduces and evaluates digital low-pass filters for eye-tracking data, showing their superiority over heuristic filters in noise reduction and providing practical guidance for offline data filtering.

Findings

Digital filters with 100 Hz cutoff outperform heuristic filters.

Heuristic filters leave noise above 100 Hz.

Digital filtering improves data quality for offline analysis.

Abstract

In 1993, Stampe [1993] suggested two "heurisitic" filters that were designed for video-oculography data. Several manufacturers (e.g., SR-Research, Tobii T60 XL and SMI) have employed these filters as an option for recording eye-movements. For the EyeLink family of eye-trackers, these two filters are referred to as standard (STD) or EXTRA. We have implemented these filters as software functions. For those who use their eye-trackers for data-collection only, this will allow users to collect unfiltered data and simultaneously have access to unfiltered, STD filtered and EXTRA filtered data for the exact same recording. Based on the literature, which has employed various eye-tracking technologies, and our analysis of our EyeLink-1000 data, we conclude that the highest signal frequency content needed for most eye-tracking studies (i.e., saccades, microsaccades and smooth pursuit) is around 100 Hz, excluding fixation microtremor. For those who collect their data at 1000 Hz or higher, we test two zero-phase low-pass digital filters, one with a cutoff of 50 Hz and one with a cutoff of 100 Hz. We perform a Fourier (FFT) analysis to examine the frequency content for unfiltered data, STD data, EXTRA filtered data, and data filtered by low-pass digital filters. We also examine the frequency response of these filters. The digital filter with the 100 Hz cutoff dramatically outperforms both heuristic filters because the heuristic filters leave noise above 100 Hz. In the paper we provide additional conclusions and suggest the use of digital filters in scenarios where offline data processing is an option.