  Mouse-tracking of computer system users represents a less expensive, but also
a far more applicable alternative to eye-tracking. The main disadvantage of
mouse-tracking are errors manifested as discrepancies between the actual
eye-gaze position and the mouse cursor position. This paper presents a method
for automated correction of errors arising in mouse-tracking. Our approach
draws upon information theory and employs Shannon entropy. The method is based
on calculating the entropy of a visual representation of a Web page, i.e., we
quantify information potential values of various positions. Information
obtained, thereby, is paired with cumulative time intervals, spent by the mouse
cursor in each position. In this way, we identify cursor positions that do not
match eye-gaze positions. To verify the effectiveness of our method, we
compared the eye gaze and mouse cursor heat maps in the following ways: We
calculated the coefficient of correlation between the two; we computed
Euclidean distance between their centers of gravity; and we performed visual
comparison.
