## 3 Experimental Results  
In this paper, we used the accuracy rate and ITR to evaluate the P300 signals detection performance on two datasets of different subjects. In dataset I, there are 85 training and 100 test characters for each subject, each of which is repeated 15 times. Tables 3 and 4 present the test accuracy rates of the proposed PCA-CNN and other classification methods in the literature, including CNN algorithms BN3 [28] and CNN-1 [24], and a traditional SVM algorithm [21], on the datasets I of subjects A and B. Bold numbers in the table indicate the best accuracy rate in the \(N(1,2,\ldots,N)\) repeats. For both subjects, the proposed PCA-CNN is one of the best algorithms in 15 repeats, and can obtain the accuracy rate higher than 95%. Furthermore, the PCA-CNN is superior to other three methods from 7 repeats for the subject A in the dataset I, and from 8 repeats for the subject B in the dataset I. In dataset II, there are 20 training characters and 30 test characters for each subject, each of which is repeated 10 times. Figure 3 presents the test accuracy rates of the proposed PCA-CNN and other classification methods in the literature, including CNN algorithms BN3 [28] and CNN-1 [24], and the traditional SVM algorithm [21], on datasets II of 10 subjects. The different color line in the figure records the results of all subjects that used different methods on each repeat. For all subjects, the average accuracy rate of PCA-CNN is higher than the other three algorithms in 4 th repeats. Furthermore, the average accuracy rate of PCA-CNN is the best algorithm in 10 repeats, and can obtain an average accuracy rate higher than 90%. The experimental results show that the PCA-CNN is superior to others in character recognition. We record the information translate rates of datasets I and II in different algorithms. As shown in Figures 4 and 5, the ITR value of the PCA-CNN is higher than that of the other three algorithms (BN3 [28], CNN-1 [24], and SVM [21]) in the maximum repeat. In Figure 4, after the 7 th repeat, the ITR value of PCA-CNN is higher than the other three algorithms (BN3 [28], CNN-1 [24] and SVM [21]). In Figure 5, after the 4 th repeat, the ITR value of PCA-CNN is higher than the other three algorithms (BN3 [28], CNN-1 [24] and SVM [21]). From the overall results of Figures 4 and 5, the PCA-CNN is faster than the CNN-1 [24] and SVM [21]. These indicate that, on the basis of ensuring the characters recognition accuracy rate, the characters' recognition speed of PCA-CNN is still fast, and the PCA-CNN algorithm has application value. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{10}{c}{**The Number of Repeats**} \\ \cline{2-13}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** \\ \hline
**PCA-CNN** & 30 & 51 & 57 & 69 & 73 & 74 & 83 & **94** & **95** & **96** & **97** & **95** & **96** & **96** & **96** \\
**BN3** & **47** & **59** & **70** & **73** & 76 & **82** & **84** & 91 & 94 & 95 & 95 & **95** & **95** & 94 & 94 & 95 \\
**CNN-1** & 35 & 52 & 59 & 68 & **79** & 81 & 82 & 89 & 92 & 91 & 91 & 90 & 91 & 92 & 92 \\
**SVM** & 35 & 53 & 62 & 68 & 75 & 80 & **84** & 86 & 89 & 91 & 92 & 93 & **96** & 95 & **96** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The accuracy rate of subject B in dataset I.