Exploring the Integration of College Vocal Music Program and College Students’ Mental Health Education in the Context of Big Data

Abstract

Abstract In this paper, we combine two feature extraction algorithms, Empirical Mode Decomposition (EMD) and wavelet packet transform, to analyze the EGG signals of students exposed to vocal music. We extract features from these signals by determining the instantaneous frequency and node signals based on the mean value of the envelope. The EGG signals were cut into short-time smooth signals. The average sample entropy value of the processed EGG signals was calculated to reflect the EEG activities of students under vocal stimulation. Then the EGG signals were used to reflect the changes in the mental health status of college students. In the vocal stimulation experiment, it was found that the students’ psychological comprehensive relaxation reached 85.47% on average, α and the amplitude of the EEG signals was able to reach about ±20. The total score of the psychological health scale of students in the experimental class after the implementation of integrated teaching reached 99.63, which was much higher than that of the control class, which was 91.26, and the difference was significant (P=0.000<0.05). Metacognitive functioning was also enhanced by the students, resulting in a 27.44% increase in the total score on the scale when compared to the pre-test results. The analysis results of this paper show that vocal music has a positive effect on the improvement of students’ psychological condition, which lays a research foundation for the integration and development of vocal music courses and mental health education.