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README.md

I2MC - MATLAB implementation

About I2MC

The I2MC algorithm was designed to accomplish fixation classification in data across a wide range of noise levels and when periods of data loss may occur.

Cite as: Hessels, R.S., Niehorster, D.C., Kemner, C., & Hooge, I.T.C. (2017). Noise-robust fixation detection in eye-movement data - Identification by 2-means clustering (I2MC). Behavior Research Methods, 49(5): 1802--1823. doi: 10.3758/s13428-016-0822-1

For more information, questions, or to check whether we have updated to a better version, e-mail: royhessels@gmail.com / dcnieho@gmail.com. I2MC is available from www.github.com/royhessels/I2MC

A Python implementation of the I2MC algorithm is available at https://github.com/dcnieho/I2MC_Python, please ensure to read the readme before using it.

Most parts of the I2MC algorithm are licensed under the Creative Commons Attribution 4.0 (CC BY 4.0) license. Some functions are under MIT license, and some may be under other licenses.

How to use

Quick start guide for adopting this script for your own data:

  1. Build an import function specific for your data (see importTobiiTX300 for an example).

  2. Change line 106 to use your new import function. The format should be: data.time for the timestamp

    data.left.X & data.left.Y for left gaze coordinates

    data.right.X & data.right.Y for right gaze coordinates

    data.average.X & data.average.Y for average of right and left gaze coordinates

    You may provide coordinates from both eyes, only the left, only the right, or only the average. Gaze coordinates should be in pixels, timestamps should be in milliseconds

  3. Adjust the variables in the "necessary variables" section to match your data

  4. Run the algorithm

Note: Signal Processing Toolbox is required for the default downsampling procedure. If not available, set opt.downsampFilter to 0. This will use a different downsampling procedure.

Tested on MATLAB R2012a, R2014b, R2016a, R2017a, & R2019b

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Noise-robust fixation classification (I2MC)

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