Emotion development system by interacting with human EEG and natural scene understanding
In this paper, we propose a mental development system for understanding the emotional status of humans, and sharing emotions with human subjects. According to the relationship between emotional factors and characteristics of an image, we incorporate the fuzzy concept to extract emotional features using L*C*H* color and orientation information. On the other hand, we also consider the EEG signals which are stimulated by natural stimuli to form the semantic emotional features as well. Emotionally relevant features are firstly clustered into two categories with degrees of belongingness to each cluster to initialize the membership functions of a neuro-fuzzy system. The IFTHEN rules of a neuro-fuzzy system to understand the positive and negative human emotions will be constructed by interacting with human. Then the system attempts to extend the number of understandable emotion. Through the time, the system sub-clusters the emotional features so that the number of membership function of the neuro-fuzzy network will increase to incorporate more complicated human expertise considering more human emotions. Using such a developmental process, the proposed system can develop a mental ability to understand more complex human emotions by mining the characteristics of emotional features and interacting with its environment.
emotion development affective computing emotion understanding electroencephalography (eeg) fuzzy c-means clustering iaps human computer interaction neuro-fuzzy inference model