Abstract: Traditional emotional evaluate methods are mainly based on changes in facial expressions and body
movements. To solve the subjective problems existing in these methods, we use positive and negative emotional
EEG (Electroencephalagrams) signal as the research object, anddesign the picture watching experiments with
the help of the CAPS (Chinese Affective Picture System) of Chinese Academy of Sciences, and then analyze the
final data using machine learning algorithm of SVM (Support Vector Machine). Moreover, according to the
actual situation of the experiment, this paper presents a new method that dividing 12 consecutive data into one set
and using the mean of each set into emotional evaluating. The results show that this new method obtainsa better
recognition accuracy of 73. 33%. This research not only provides a method that reduces the error caused by
emotion generating delay, but also provides a reference for the analysis and optimization of brain waves.

Key words:  data mining, emotional evaluation, support vector machine (SVM), electroencephalagram(EEG)