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@ARTICLE{Wagner:865042,
      author       = {Wagner, Adina Svenja and Halchenko, Yaroslav and Hanke,
                      Michael},
      title        = {multimatch-gaze: {T}he {M}ulti{M}atch algorithm for gaze
                      path comparison in {P}ython},
      journal      = {The journal of open source software},
      volume       = {4},
      number       = {40},
      issn         = {2475-9066},
      reportid     = {FZJ-2019-04604},
      pages        = {1525 -},
      year         = {2019},
      abstract     = {Multimatch-gaze is a Python package for computing the
                      similarity of eye-movement sequences, so called scan paths.
                      Scan paths are the trace of eye-movements in space and time,
                      usually captured with eye tracking devices. Scan path
                      similarity is a measure that is used in a variety of
                      disciplines ranging from cognitive psychology, medicine, and
                      marketing to human-machine interfaces. In addition to
                      quantifying position and order of a series of eye-movements,
                      comparing their temporo-spatial sequence adds an insightful
                      dimension to the traditional analysis of eye tracking data.
                      It reveals commonalities and differences of viewing behavior
                      within and between observers, and is used to study how
                      people explore visual information. For example, scan path
                      comparisons are used to study analogy-making (French, Glady,
                      $\&$ Thibaut, 2017), visual exploration and imagery
                      (Johansson, Holsanova, $\&$ Holmqvist, 2006), habituation in
                      repetitive visual search (Burmester $\&$ Mast, 2010), or
                      spatial attention allocation in dynamic scenes (Mital,
                      Smith, Hill, $\&$ Henderson, 2011). The method is applied
                      within individuals as a measure of change (Burmester $\&$
                      Mast, 2010), or across samples to study group differences
                      (French et al., 2017).Therefore, in recent years, interest
                      in the study of eye movement sequences has sparked the
                      development of novel methodologies and algorithms to perform
                      scan path comparisons. However, many of the contemporary
                      scan path comparison algorithms are implemented
                      inclosed-source, non-free software such as
                      Matlab.Multimatch-gaze is a Python-based reimplementation of
                      the MultiMatch toolbox for scanpath comparison, originally
                      developed by Jarodzka, Holmqvist, $\&$ Nyström (2010) and
                      implemented by Dewhurst et al. (2012) in Matlab. This
                      algorithm represents scan paths asgeometrical vectors in a
                      two-dimensional space: Any scan path is built up of a
                      coordinate vector sequence in which the start and end
                      position of vectors represent fixations, and the vectors
                      represent saccades. Two such vector sequences are, after
                      optional simplification based on angular relations and
                      amplitudes of saccades, compared on the five dimensions
                      “vector shape”, “vector length (amplitude)”,
                      “vector position”, “vector direction”, and
                      “fixation duration” for a multidimensional similarity
                      evaluation.This reimplementation in Python aims at providing
                      an accessible, documented, and tested open source
                      alternative to the existing MultiMatch toolbox. The
                      algorithm is an established tool for scan path comparison
                      (N. C. Anderson, Anderson, Kingstone, $\&$ Bischof,
                      2015),and improved availability aids adoption in a broader
                      research community. multimatch-gaze is available from its
                      Github repository and as the Python package multimatch-gaze
                      via pip install multimatch-gaze. The module contains the
                      same functionality as the original Matlab toolbox, that is,
                      scan path comparison with optional simplification according
                      to userdefined thresholds, and it provides this
                      functionality via a command line interface or a PythonAPI.
                      Data for scan path comparison can be supplied as nx3
                      fixation vectors with columns corresponding to
                      x-coordinates, y-coordinates, and duration of the fixation
                      in seconds (as for the original Matlab toolbox).
                      Alternatively, multimatch-gaze can natively read in event
                      detection output produced by REMoDNaV (Dar, Wagner, $\&$
                      Hanke, 2019), a velocity-based eye movement classification
                      algorithm written in Python. For REMoDNaV-based input, users
                      can additionally specify whether smooth pursuit events in
                      the data should be kept in the scan path or discarded.},
      cin          = {INM-7},
      ddc          = {004},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574)},
      pid          = {G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.21105/joss.01525},
      url          = {https://juser.fz-juelich.de/record/865042},
}