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With the continuous development of brain-computer interface technology, as a typical brainwave signal carrying brain state information, has entered the researcher’s point of view. Brain waves carry detailed information about the state of the brain. [1]Therefore, the use of computers to collect and analyze EEG signals play an important role in the study of artificial intelligence. In this paper, the EEG signal of positive and negative emotions is taken as the research object.[2] Firstly, the present situation of brain wave research is introduced, and the image is experimented with the Chinese emotional image system (CAPS) of Chinese Academy of Sciences. Based on the previous work of “Emotional Evaluation Based on SVM”, further data analysis was carried out using support vector machines (SVM) and linear machines with linear and nonlinear cores, and 49.2% to 62.4% accuracy were obtained respectively. This paper provides a feasible solution for the study of EEG in the field of emotional analysis.Finally, through the accuracy of the linear algorithm and the nonlinear algorithm on the data, we find that the EEG data are non-linear separable

The Discussion of the Effects of Linear and Nonlinear Algorithms on Emotional Evaluation