Prediction Properties of Attractors Based on Their Fuzzy Trend

Nowadays the developers of robot and automated systems face the problem of analysis and interpretation of different signals, which reflect physiological processes in a human body. This is connected with increasing requirements to the means of effective support of interactions between users and computers. One of the ways to solve this problem is using the models of human emotions (operator), who takes part in forming or monitoring controlling actions in an automated system. The authors propose approaches to monitoring human emotional states using assessment of a limited number of characteristics of speech samples or electroencephalograph (EEG) signals. In order to analyze and interpret these signals the authors use methods of nonlinear dynamics, which allow reconstructing an attractor using a limited time series fragment. The paper describes a test procedure and the results, which show the changes of attractor properties during the influence of auditory incentive bunches with the same emotional interpretation on an operator. The article presents a transition sequence from one attractor to another as a fuzzy time series. Each time series is based on the characteristics of one attractor (point density of trajectories in its center surroundings). The estimates of attractor sizes allow defining an emotion sign. The direction of emotional reaction development is determined based on fuzzy estimates of an attractor density increment sign, when the attractors are reconstructed for two subsequent watch windows. Fuzzy estimates of density increment of three attractors, which are reconstructed for three subsequent watch windows, determine the trend of testee’s emotional state. There follows the prediction of direction of operator’s emotional state development. The paper shows the results of using the algorithm for analysis of EEG attractors when listening to (a) one musical incentive, (б) several bunches of incentive.