The objective of this work was the implementation of a computational algorithm that allowed the electrooculographics
signals analysis, which represented different types of saccadics and antisaccadics movements. A study about the nature
of the electrooculographics signals was made to determine the signals frequency composition. This study yielded that the
electrooculographics signals studied mainly have low frequency elements, although they possess elements belonging to a
wide range of high frequencies, but that as a whole, these elements constitute a smaller part of the signals.
Computational routines were developed which allowed filtering the signals to eliminate the noise that could be contained
in the signal and improve the quality of the original signal. In the implementation of these routines, two mathematical
algorithms were used: Wavelet Transform and the Parks-McClellan or Remez algorithm for the low pass filters design.
The results showed that it was faster filtering the signal with the Wavelet Transform, but the filters designed with the
Parks-McClellan algorithm did not distort the original signal in an appreciable way.
Once filtered the signals the characterization takes place, which consisted on the automatic detection of the regions
corresponding to saccades. In this detection was used another Wavelet Transform application, as is the locating of abrupt
changes in the signals. It was carried out the determination of the parameters from each saccade: Amplitude, Duration,
Gain, Peak Velocity and Latency. Some important relations between the saccades parameters of the electrooculographics signals were obtained and others found by different authors were verified.