Abstract
Based on the study of the feature extraction algorithm based on the multiple empirical mode decomposition of the Duffing equation, this paper proposes a corresponding improved algorithm, completes the identification and analysis of the psychological pressure dimension space under the audiovisual induction method, and designs two typical psychological types of music and pictures. Based on the stress induction experiment, an audiovisual-induced psychological stress recognition system based on EEG (electroencephalogram) signals was built. Aiming at the problem that the spatial uniform sampling method cannot well reflect the dynamic characteristics of the multivariate EEG signal, based on the Duffing equation, a nonuniform sampling algorithm that adaptively selects the projection direction is proposed. At present, the use of the Duffing equation to detect weak unknown signals is to select a set of fixed parameters. Analysis of these two aspects to determine the parameters of the system is based on the parameter analysis of the Duffing equation oscillator. Due to the sensitivity of the Duffing equation to the initial value, the choice of parameters has a great influence on the detection effect. In response to this situation, the relationship between the parameters and initial values of the Duffing equation is analyzed. From the relationship between the parameters and the initial values, the influence of different parameters on the detection effect is analyzed to verify the superiority of the current equation parameters. First, the multichannel EEG signal is nonuniformly sampled multiempirical modal decomposition, and an effective intrinsic modal function is selected to extract the mental stress EEG characteristics. Experimental results show that the EEG signal recognition algorithm based on the Duffing equation effectively extracts EEG signal features and improves the classification accuracy of mental stress EEG signals.
Highlights
Psychological stress is the psychological and physiological state that is produced along with the process of cognition and consciousness
Among the various information sources that can be used for psychological stress recognition, EEG signals are not easy to disguise and sensitive, and the recognition results are objective and true, which are the current research hotspots [2]
With the development of the brain-computer interface (BCI) and the advancement of artificial intelligence, the identification of psychological stress through EEG signals has become a new method for studying psychological stress [4]
Summary
Psychological stress is the psychological and physiological state that is produced along with the process of cognition and consciousness. There are more researches on the identification of basic psychological stress under the discrete model, and there are fewer studies based on the dimensional model, and the classification accuracy rate is not high It is mainly for the induction method of a single type of stimulus. In addition to the recognition of psychological stress through certain external manifestations of people, physiological signals such as heartbeat, respiration, body temperature, skin electrophysiology, and brain electricity are widely used in the field of psychological stress recognition [9]. Most of these physiological signals can be acquired by noninvasive collection methods, so they can be collected more conveniently under a variety of environmental conditions. We analyze the abruptness of the critical point phase diagram of the system and the retention of the chaotic interval from different parameters
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