fixation_VTI: Fixation detection using a velocity threshold identification...

In tombeesley/eyetools: Analyse Eye Data

Fixation detection using a velocity threshold identification method

Description

Determine fixations by assessing the velocity of eye-movements, using a method that is similar to that proposed by Salvucci & Goldberg (1996). Applies the algorithm used in VTI_saccade and removes the identified saccades before assessing whether separated fixations are outside of the dispersion tolerance. If they are outside of this tolerance, the fixation is treated as a new fixation regardless of the length of saccade separating them. Compared to fixation_dispersion(), fixation_VTI() is more conservative in determining a fixation as smaller saccades are discounted and the resulting data is treated as a continued fixation (assuming it is within the pixel tolerance set by disp_tol). Returns a summary of the fixations found per trial, including start and end coordinates, timing, duration, mean velocity, and peak velocity.

Usage

fixation_VTI(
  data,
  sample_rate = NULL,
  threshold = 100,
  min_dur = 150,
  min_dur_sac = 20,
  disp_tol = 100,
  smooth = FALSE,
  progress = TRUE,
  participant_ID = "participant_ID"
)

Arguments

data	A dataframe with raw data (time, x, y, trial) for one participant
sample_rate	sample rate of the eye-tracker. If default of NULL, then it will be computed from the timestamp data and the number of samples
threshold	velocity threshold (degrees of VA / sec) to be used for identifying saccades.
min_dur	Minimum duration (in milliseconds) of period over which fixations are assessed
min_dur_sac	Minimum duration (in milliseconds) for saccades to be determined
disp_tol	Maximum tolerance (in pixels) for the dispersion of values allowed over fixation period
smooth	include a call to eyetools::smoother on each trial
progress	Display a progress bar
participant_ID	the variable that determines the participant identifier. If no column present, assumes a single participant

Details

It can take either single participant data or multiple participants where there is a variable for unique participant identification. The function looks for an identifier named participant_ID by default and will treat this as multiple-participant data as default, if not it is handled as single participant data, or the participant_ID needs to be specified

Value

a dataframe containing each detected fixation by trial, with mean x/y position in pixel, start and end times, and duration.

References

Salvucci, D. D., & Goldberg, J. H. (2000). Identifying fixations and saccades in eye-tracking protocols. Proceedings of the Symposium on Eye Tracking Research & Applications - ETRA '00, 71–78.

Examples

data <- combine_eyes(HCL)
data <- interpolate(data, participant_ID = "pNum")
fixation_VTI(data[data$pNum == 119,], participant_ID = "pNum")

tombeesley/eyetools documentation built on Dec. 23, 2024, 12:36 a.m.