With the rapid development of society, the number of college students in our country is on the rise. College students are under\npressure due to challenges from the society, school, and family, but they cannot find a suitable solution. As a result, the\npsychological problems of college students are diversified and complicated. The mental health problem of college students is\nbecoming more and more serious, which requires urgent attention.This article realizes the monitoring of university mental health\nby identifying and analyzing the emotions of college students. This article uses EEG to determine the emotional state of college\nstudents. First, feature extraction is performed on different rhythm data of EEG, and then a fuzzy support vector machine (FSVM)\nis used for classification. Finally, a decision fusion mechanism based on the D-S evidence combination theory is used to fuse the\nclassification results and output the final emotion recognition results. The contribution of this research is mainly in three aspects.\nOne is the use of multiple features, which improves the efficiency of data use; the other is the use of a fuzzy support vector machine\nclassifier with higher noise resistance, and the recognition rate of the model is better. The third is that the decision fusion\nmechanism based on the D-S evidence combination theory takes into account the classification results of each feature, and the\nclassification results assist each other and integrate organically. The experiment compares emotion recognition based on single\nrhythm, multirhythm combination, and multirhythm fusion. The experimental results fully prove that the proposed emotion\nrecognition method can effectively improve the recognition efficiency. It has a good practical value in the emotion recognition of\ncollege students.
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