# Model of Emotion Judgment Based on Features of Multiple Physiological Signals
## 4 Extraction of Optimal Feature and Model of Emotion Judgment
### Extraction of Optimal Feature Set  
Firstly, SPSS (statistical product and service solutions) software is used to make selection of dimensionality reduction for the features through PCA, and then the SVM is used to recognize the samples. Finally, the recognition rate is used to determine the most effective feature subset as the basis for model establishment. A total of 15 principal components as shown in Table 4 are obtained after transforming the original feature matrix using the method of PCA. The cumulative contribution rate of these principal components has reached 86.3% of the total contribution rate, which meets the condition that cumulative contribution rate of principal component is greater than 85%. Then, the weight threshold of each feature of physiological signal on the principal component is taken as the criterion for selecting feature. Finally, some original features that play a major role can be determined as optimal feature subset to build a judgment model. Since the weight of all signal features in the principal component is less than 7%, the weight threshold of feature is set to 1%, 2%, 4%, and 6%. A total of 660 samples are randomly extracted from each emotion trend during classification and recognition, and there are 2640 samples for four emotion trends. Then, 1740 samples were randomly selected from the 2640 samples as the training set, and the remaining 900 samples were used as test set. Each round of recognition is performed for 20 times, and the final recognition rate is the average of 20 recognition rates. The results of emotion recognition are shown in Table 5 where we can see that the best recognition effect can be obtained when the weight threshold is 4%. Under this circumstance, the 20 kinds of features that achieve the best recognition result include: (1) skin electrical signals: range, mean value of first order difference, minimum ratio of first order difference, the first order standard difference, minimum of first order difference, minimum ratio, maximum ratio, mean value, minimum ratio of second order difference, mean value of frequency domain; (2) ECG: mean value of R-wave, range of R-wave, mean value of Q-wave, standard difference of S-wave, percentage of RR interval difference >50 ms; (3) pulse wave signals: amplitude of main wave, mean value of main wave, range of main wave, time from start to peak of main wave, percentage of main wave interval >50 ms. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Characteristic Root**} & \multicolumn{2}{c}{**Contribution of Principal**} & \multicolumn{2}{c}{**Cumulative**} \\  & & **Component (\%)** & **Contribution (\%)** \\ \hline
1 & 4.1365 & 8.113 & 8.113 \\ \hline
2 & 4.0774 & 8.005 & 16.118 \\ \hline
3 & 3.6982 & 7.107 & 23.225 \\ \hline
4 & 3.5540 & 6.628 & 29.853 \\ \hline
5 & 3.3061 & 6.449 & 36.302 \\ \hline
6 & 2.9845 & 6.012 & 42.314 \\ \hline
7 & 2.9346 & 5.901 & 48.215 \\ \hline
8 & 2.8651 & 5.582 & 53.797 \\ \hline
9 & 2.8500 & 5.300 & 59.097 \\ \hline
10 & 2.8306 & 5.256 & 64.353 \\ \hline
11 & 2.7640 & 5.002 & 69.355 \\ \hline
12 & 2.5364 & 4.860 & 74.215 \\ \hline
13 & 2.3205 & 4.306 & 78.521 \\ \hline
14 & 2.1150 & 4.033 & 82.554 \\ \hline
15 & 2.0684 & 3.775 & 86.329 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Principal component table. After obtaining 20 kinds of features with the best recognition effect, the Pearson correlation coefficient (PCC) is used to judge the relationship between the emotional interval and these features and build the model of emotion recognition. The PCC is obtained by dividing the covariance by the standard deviation of two numbers. The covariance is a numerical feature that represents the relationship between two variables \(X\) and \(Y\):  
\[\text{Cov}(X,Y)=\frac{1}{n-1}{\sum\limits_{1}^{n}{(X_{i}-\overline{X})(Y_{i}- \overline{Y})}} \tag{12}\]  
the change trend of \(X\) and \(Y\) is the same when \(X=Y\).