Filtering eye-tracking data from an EyeLink 1000: Comparing heuristic, savitzky-golay, IIR and FIR digital filters
In a prior report (Raju et al., 2023) we concluded that, if the goal was to preserve events such as saccades, microsaccades, and smooth pursuit in eye-tracking recordings, data with sine wave frequencies less than 75 Hz were the signal and data above 75 Hz were noise. Here, we compare five filters...
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MDPI AG
2023-10-01
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| Series: | Journal of Eye Movement Research |
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| Online Access: | https://bop.unibe.ch/JEMR/article/view/9888 |
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| author | Mehedi Hasan Raju Lee Friedman Troy Bouman Oleg Komogortsev |
| author_facet | Mehedi Hasan Raju Lee Friedman Troy Bouman Oleg Komogortsev |
| author_sort | Mehedi Hasan Raju |
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In a prior report (Raju et al., 2023) we concluded that, if the goal was to preserve events such as saccades, microsaccades, and smooth pursuit in eye-tracking recordings, data with sine wave frequencies less than 75 Hz were the signal and data above 75 Hz were noise. Here, we compare five filters in their ability to preserve signal and remove noise. We compared the proprietary STD and EXTRA heuristic filters provided by our EyeLink 1000 (SR-Research, Ottawa, Canada), a Savitzky-Golay (SG) filter, an infinite impulse response (IIR) filter (low-pass Butterworth), and a finite impulse filter (FIR). For each of the non-heuristic filters, we systematically searched for optimal parameters. Both the IIR and the FIR filters were zero-phase filters. All filters were evaluated on 216 fixation segments (256 samples), from nine subjects. Mean frequency response profiles and amplitude spectra for all five filters are provided. Also, we examined the effect of our filters on a noisy recording. Our FIR filter had the sharpest roll-off of any filter. Therefore, it maintained the signal and removed noise more effectively than any other filter. On this basis, we recommend the use of our FIR filter. We also report on the effect of these filters on temporal autocorrelation.
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| format | Article |
| id | doaj-art-8cb9e78dcfdc4d5abd5180d908bdee9f |
| institution | OA Journals |
| issn | 1995-8692 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Eye Movement Research |
| spelling | doaj-art-8cb9e78dcfdc4d5abd5180d908bdee9f2025-08-20T02:09:35ZengMDPI AGJournal of Eye Movement Research1995-86922023-10-0114310.16910/jemr.14.3.6Filtering eye-tracking data from an EyeLink 1000: Comparing heuristic, savitzky-golay, IIR and FIR digital filtersMehedi Hasan Raju0Lee Friedman1Troy Bouman2Oleg Komogortsev3Texas State UniversityTexas State University, San Marcos, TX-78666Michigan Technological University, Houghton, MITexas State University, San Marcos, TX-78666 In a prior report (Raju et al., 2023) we concluded that, if the goal was to preserve events such as saccades, microsaccades, and smooth pursuit in eye-tracking recordings, data with sine wave frequencies less than 75 Hz were the signal and data above 75 Hz were noise. Here, we compare five filters in their ability to preserve signal and remove noise. We compared the proprietary STD and EXTRA heuristic filters provided by our EyeLink 1000 (SR-Research, Ottawa, Canada), a Savitzky-Golay (SG) filter, an infinite impulse response (IIR) filter (low-pass Butterworth), and a finite impulse filter (FIR). For each of the non-heuristic filters, we systematically searched for optimal parameters. Both the IIR and the FIR filters were zero-phase filters. All filters were evaluated on 216 fixation segments (256 samples), from nine subjects. Mean frequency response profiles and amplitude spectra for all five filters are provided. Also, we examined the effect of our filters on a noisy recording. Our FIR filter had the sharpest roll-off of any filter. Therefore, it maintained the signal and removed noise more effectively than any other filter. On this basis, we recommend the use of our FIR filter. We also report on the effect of these filters on temporal autocorrelation. https://bop.unibe.ch/JEMR/article/view/9888Eye movementsignalnoisefilterautocorrelation |
| spellingShingle | Mehedi Hasan Raju Lee Friedman Troy Bouman Oleg Komogortsev Filtering eye-tracking data from an EyeLink 1000: Comparing heuristic, savitzky-golay, IIR and FIR digital filters Journal of Eye Movement Research Eye movement signal noise filter autocorrelation |
| title | Filtering eye-tracking data from an EyeLink 1000: Comparing heuristic, savitzky-golay, IIR and FIR digital filters |
| title_full | Filtering eye-tracking data from an EyeLink 1000: Comparing heuristic, savitzky-golay, IIR and FIR digital filters |
| title_fullStr | Filtering eye-tracking data from an EyeLink 1000: Comparing heuristic, savitzky-golay, IIR and FIR digital filters |
| title_full_unstemmed | Filtering eye-tracking data from an EyeLink 1000: Comparing heuristic, savitzky-golay, IIR and FIR digital filters |
| title_short | Filtering eye-tracking data from an EyeLink 1000: Comparing heuristic, savitzky-golay, IIR and FIR digital filters |
| title_sort | filtering eye tracking data from an eyelink 1000 comparing heuristic savitzky golay iir and fir digital filters |
| topic | Eye movement signal noise filter autocorrelation |
| url | https://bop.unibe.ch/JEMR/article/view/9888 |
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