Input data (OGAMA) Info ScanGraph application is designed to work with data exported from open-source application OGAMA ( The user can calculate similarity according to a regular grid or defined Areas of Interest.
In Scanpath module, OGAMA contains a tool called “Levenshtein Distance Calculation”, which is capable of computation of Levenshtein distances between participants’ trajectories.
For the use of ScanGraph application, only Subject names (1), Scanpath strings (2) and affiliation to subject groups (3) will be used.
Text file exported from OGAMA can be directly used in ScanGraph.

Note: Uploaded data are stored anonymously at our server and will not be used for any other purposes.

Source: sampledata.txt

Define method String-edit distance method
Info The similarity can be calculated using Levenshtein, Damerau-Levenshtein, or Needleman-Wunsch algorithm. All algorithms were modified to better work with sequences with different lengths.
Levenshtein distance algorithm counts a number of needed changes (deletion, substitution, insertion) from one sequence to another.
Damerau-Levenshtein algorithm counts a number of needed changes (deletion, substitution, insertion, transposition) from one sequence to another. Needleman-Wunsch algorithm seeks the number of identical elements of the pair of sequences.
Calculation of Needleman-Wunsch and Damerau-Levenshtein algorithms take more time, the user has to wait for the result (in some cases more than 30 seconds).

Info Using this button, the user can choose between original and collapsed fixation sequences. In collapsed sequences, there are no successive AOIs in the sequence.

Collapsed: ABCD

User defined graph Use parameter p <0,1>
Info The parameter p takes value from the interval <0,1> and represents the degree of similarity.
The higher value of p, the higher similarity of the given sequences.

Info Percent of edges takes value from the interval <0,100>. The complete graph contains 100 % of edges.
According to our experiences, the value 5 % has a very high interpretative value about the similarities.