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Problems in the analysis of gaze data R package saccades

I got eye tracking gaze data in the form of x/y coordinates and timestamps.

Now I want to plot the saccades using the R package saccades. Unfortunately, it doesn't work. I guess it's a matter of having the data in the wrong format.

My data:

head(EUFKDCDL_Q09AS_saccades)
# A tibble: 6 x 4
           time     x     y trial
          <dbl> <dbl> <dbl> <dbl>
1 1550093577941   732   391     1
2 1550093577962   706   320     1
3 1550093577980   666   352     1
4 1550093578000   886   288     1
5 1550093578017   787   221     1
6 1550093578037   729   302     1

The code that didn't work:

fixations <- detect.fixations(EUFKDCDL_Q09AS_saccades)

Error in detect.fixations(EUFKDCDL_Q09AS_saccades) : No saccades were detected. Something went wrong.

The full code that shouldwork according github (https://github.com/tmalsburg/saccades):

library(saccades)
data(samples)
head(samples)
  time     x      y trial
1    0 53.18 375.73     1
2    4 53.20 375.79     1
3    8 53.35 376.14     1
4   12 53.92 376.39     1
5   16 54.14 376.52     1
6   20 54.46 376.74     1

fixations <- detect.fixations(samples)
head(fixations[c(1,4,5,10)])
  trial        x         y  dur
0     1 53.81296 377.40741   71
1     1 39.68156 379.58711  184
2     1 59.99267 379.92467   79
3     1 18.97898  56.94046  147
4     1 40.28365  39.03599  980
5     1 47.36547  35.39441 1310

diagnostic.plot(samples, fixations)

So there must be a problem with how my data is structured I guess? What does the mean?

I hope that any of you can help me creating this saccade plot as in the sceenshot attached

saccades plot

I am an R beginner.

Solution

  • Before you attempt to analyse your data, you must visualise it. This will help you see if the data is actually what you think it is, and to show qualitatively the extent of some data quality issues.

    If your data is actually a gaze data time series, then you could visualise it as in your example above simply by plotting x as a function of time and y as a function of time. This would not require that you run the data through a saccade detection algorithm first. The plot you show above is simply a visualisation of the raw data, with the saccade detections superimposed (the grey lines), but that is an optional step.

    Now to your raw data: if you plot it as suggested, I suspect it isn't going to be what you think it is. Compare it to the example data that you posted from https://github.com/tmalsburg/saccades. That dataset shows a couple of characteristics:

    • The raw data is given with sub-pixel precision (i.e. to 2 decimal places). This is typical of calibrated eye data, where the estimated gaze signal at any point is the output of a calibration process that maps coordinates from the original eye video data to the viewed image coordinates, via a continuous function that yields interpolated values that appear to exceed the resolution of those images. Your data, however, consists of integers, which is unusual (but possible).
    • More importantly, the Github example data also shows that each sample position varies very little from its preceding one (e.g. the x position shifting from 53.18 to 53.20 from one sample to the next). This is typical of real gaze data during a fixation. During a saccade, adjacent samples start changing much more rapidly of course, and you can often detect them visually simply by scrolling down a column of values and noting where the adjacent values change quickly. Compare this to your data, which is completely different: values bounce up and down erratically from one sample to the next (e.g. from 732 to 706 to 666 to 886 in successive samples). This is not typical of a good gaze data signal. So plot your time series and see what you actually have there.

    I suspect that there are discontinuities in your data, and this will cause any saccade or fixation detection algorithm to fail. Try to figure out what your data actually is representing, and/or what the quality issues are, before attempting to parse into saccades and fixations.

    Hint: using the ggplot2 library to visualise your data:

    library(ggplot2)

ggplot(data = EUFKDCDL_Q09AS_saccades,
       aes(x = time)) + # define time series x axis
geom_line(aes(y = x), colour = 'red') +  # plot horizontal data
geom_line(aes(y = y), colour = 'yellow') # plot vertical time series too

    If ggplot2 isn't installed, call install.packages('ggplot2') first.