Increase the sampling frequency to 1000Hz. See https://dr-jt.github.io/pupillometry/ for more information.
pupil_upsample(x)dataframe.
Inserts additional rows into the data with missing pupil and gaze values. Adds a column, `UpSampled` to identify whether the data has been up-sampled.
There are some advantages to up-sampling the data to a sampling frequency of 1000Hz, and is even a recommended step in preprocessing by Kiret & Sjak-Shie (2019).
Up-sampling, should occur before smoothing and interpolation. In general, it is safer to apply smoothing before interpolation (particularly if cubic-spline interpolation is to be used). However, if up-sampling is to be used, interpolation needs to occur first in order to fill in the missing up-sampled values. The question, then, is how can we apply smoothing first while still doing up-sampling?
This is resolved in this package by first up-sampling with `pupil_upsample()` and then smoothing `pupil_smooth()`. `pupil_upsample()` will not interpolate the missing up-sampled values. Instead, a linear interpolation will be done in`pupil_smooth()`, if `pupil_upsample()` was used prior, followed by smoothing and then after smoothing, originally missing values (including the missing up-sampled values and missing values due to blinks and other reasons) will replace the linearly interpolated values (essentially undoing the initial interpolation). After `pupil_smooth()`, interpolation can then be applied to the up-sampled-smoothed data with `pupil_interpolate()`.
This is all to say that, the intuitive workflow can still be used in which, `pupil_upsample()` is used, followed by `pupil_smooth()`, followed by `pupil_interpolate()`.
Alternatively, to interpolate before smoothing, `pupil_upsample()` is used, followed by `pupil_interpolate()`, followed by `pupil_smooth()`. The difference being that, in this case, no interpolation and then replacing the missing values back in the data is done in `pupil_smooth()` because interpolation was performed first anyways.