Cluster Fix for Matlab

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General Description

Eye tracking is used extensively in research to analyze scan paths in humans and animal subjects. The most common algorithms for analyzing scan paths employ velocity and acceleration thresholds to determine when a saccade occurs since the eye moves faster during a saccade than during a fixation; however, these thresholds are often arbitrary as well as inaccurate. To solve these serious flaws, Cluster Fix uses k-means cluster analysis on distance, velocity, acceleration, and angular velocity. K-means clustering finds natural divisions in data and groups the data in K number of clusters. The number of clusters is determined using a built-in MATLAB function which optimizes the distances between data points within a cluster relative to the separation of clusters in space. Initially, Cluster Fix globally evaluates the whole scan path at once, detects fixations and saccades, and then locally re-evaluates each fixation and saccade pair in order to identify small, short saccades as well as precisely identify the start and end of saccades. Cluster Fix is written in MATLAB code. 

Cluster Fix publication provides more detail.

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Copyright © 2013-2014, Seth Koenig & Elizabeth Buffalo. All rights reserved.

If utilization of the Cluster Fix algorithm, with or without modification, results in outcomes which will be published, you must site the following publication: 

Seth D. König, and Elizabeth A. Buffalo. A nonparametric method for detecting fixations and saccades using cluster analysis: Removing the need for arbitrary thresholds. Journal of Neuroscience Methods, 2014. 

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