Evaluation of psychological stress level based on speech and EEG signal
In order to improve the accuracy of pressure classification method.realize the deep mining of multi-modal information interaction and multi-dimensional three-dimensional fusion features,a multi-modal pressure identification method based on model classification is proposed.A new psychological stress index model is constructed based on the amplitude characteristics of speech signals and the amplitude characteristics of each frequency band of EEG signals,and a psychological stress classification method for the model is proposed to solve the problems of limited subjective assessment accuracy and unclear stress classification basis.The labels of MAHNOB-HCI data set are reconstructed based on the model classification,and the multi-dimensional stereo fusion features containing EEG time-frequency-space information and speech time-frequency information are constructed to solve the problem of missing pressure information caused by the single feature research method.Compared with the single modal method,the recognition accuracy of the proposed method is increased by 10.72%and 3.36%,respectively.Compared with the conventional dual-modal method,the recognition accuracy is increased by 7.51%.To sum up,the proposed method can more accurately reveal the relationship between the full-band information of heterogeneous data and psychological stress,and effectively improve the recognition performance.
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