FACIAL GESTURE CHARACTERIZATION FROM ELECTROENCEPHALOGRAPHIC SIGNALS

Study of the activity of human brain has advanced plenty since the development of electroencephalogram. A few decades ago, Brain Computer Interfaces (BCI), which allow direct communication between the brain and computer systems, seemed to belong only to science fiction. For example, today a limb of the body can be replaced by a robotic limb and it can be controlled using current nerve impulses. Analysis and interpretation of electroencephalographic signals represents a challenge since everyone responses differently to the same stimuli depending on several factors such as age, sex, diseases, among others. In this article the implementation of a BCI for the acquisitions of electroencephalographic signals is performed by using sensors present on the EMOTIV INSIGHT headset to visualize, process and analyze signals emitted by the brain when someone is performing certain facial gestures, with aiming to characterizing them using correlation analysis. This study evaluates the strength of a relationship between the signals produced by the same facial gestures and different test subjects. The correlation analysis, despite being a statistical method, proved to have significant results, obtaining an average of 20.91% of strong relationships and 34.18% of moderate relationships