# Human-Computer Interaction Multi-Task Modeling Based on Implicit Intent EEG Decoding
## 5 Results and Analysis  
### Classification Results  
The feature data extracted by the CSP algorithm were input into SVM, KNN, and GNB classifiers, respectively. The RBF kernel SVM classifier was adopted, and we performed a grid search on c and g to find the optimal values (the values of c and g are from \(10^{-5}\) to \(10^{5}\), step size is 0.5) by cross-validation of the training set. The optimal SVM-RBF parameter combination was obtained by using 5-fold cross-validation for each pair of c and g. In the KNN model, Euclidean distance was used to measure the distance between samples, and a 5-fold cross-validation method was used to calculate the training performance of the KNN model in the range of k values from 3 to 11, and the optimal k value was obtained. The training performance and optimal parameters of the classifier are shown in Table 1.  
The trained optimal parameters and model were used to identify the data in the test set. The test accuracy of 20 subjects is shown in Table 2. As can be seen from Table 3, the average test accuracy of the SVM classifier is the highest, which is 90.81% \(\pm\) 9.42. The second is the KNN classifier, whose test accuracy is 80.87% \(\pm\) 5.94. Among the three classifiers, the GNB classification accuracy is the lowest, at only 76.66% \(\pm\) 10.12. The Friedman test was used to compare the three classifiers and observe the performance differences. The Friedman test results showed that the three classifiers were significantly different (\(p\) = 0.001, \(\chi\)2 = 14, df = 2). A post hoc test showed that the SVM classifier had significantly better performance than the other two classifiers, as shown in Figure 3.  
In terms of the recognition accuracy of different interaction intentions, the SVM model has the highest recognition accuracy for each intention task; the average recognition accuracy of the task is 90.5%; the recognition accuracy of task 1 is the highest, reaching 99.48%, followed by the recognition accuracy of task 3 of 90.96%. The recognition accuracy of tasks 5, 4, and 2 is 89.81%, 87.62%, and 84.63%, respectively. The comparison of the recognition accuracy of the three classifiers is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**SVM**} & \multicolumn{3}{c}{**GaussianNB**} \\  & \multicolumn{2}{c}{**(RBF Kernel)**} & \multicolumn{2}{c}{**KNN**} & \multicolumn{2}{c}{**GaussianNB**} \\  & \multicolumn{1}{c}{**\%Acc**} & \multicolumn{1}{c}{**c} & \multicolumn{1}{c}{**c}{**c**} & \multicolumn{1}{c}{**g**} & \multicolumn{1}{c}{**\%Acc**} & \multicolumn{1}{c}{**k**} & \multicolumn{1}{c}{**\%Acc**} \\ \hline S1 & 99.10 & 0.25 & 8 & 86.75 & 5 & 84.52 \\ S2 & 99.08 & 0.25 & 8 & 85.77 & 4 & 82.11 \\ S3 & 99.54 & 0.25 & 8 & 88.22 & 4 & 85.21 \\ S4 & 99.09 & 0.125 & 4 & 85.87 & 4 & 85.14 \\ S5 & 98.94 & 0.25 & 8 & 87.01 & 3 &Among the five types of interactive intention, the recognition accuracy of generalized browsing intention is the highest. The reason is that this intention is the task with the smallest task load, and the user behavior is closer to the resting EEG, so it is the most different from other types of intention. This is followed by click control intent, table query intent, and complex disposal intent. The target search intention has the lowest recognition accuracy among the five types of interaction intentions, with the average recognition accuracy of the three classifiers, indicating that the interaction task is not sufficiently distinguishable from other tasks, especially from the generalized browsing intention, which