The Influence of Beta Signal toward Emotion Classification for Facial Expression Control through EEG Sensors

The important role of communication process between human and computer have been increased in the recent years. In this paper, the main focus on analyzing human brain signals to create a natural interaction between human brain and virtual human. The method of this study base on reading brain signals then classifies the signals in order to represent it as an avatar facial expression. These signals are associated with the inner emotion of the user. In order to get a real interaction between the internal emotion of a user and avatar facial expression, signal speed is used to clarify the difference of user situation for two emotions: happy and sad. The interactive process based on the relationship between human emotion and the velocity of brain signal from the result ΔV for Z1 is faster than ΔV for Z2. In this case, the velocity of sad emotion will be faster than happy emotion. As a result, this study shows a range of speed for each emotion which can be used to specify and represent the internal emotion of a user to create a natural interaction with virtual human. These results can be more realistic because it specifies the average of speed for each emotion.