# Human-Computer Interaction Multi-Task Modeling Based on Implicit Intent EEG Decoding
## 5 Results and Analysis
### Effectiveness of the CSP Intention Feature  
In order to verify the superiority of the common space pattern algorithm in interactive intention feature extraction compared with other feature extraction methods, this paper compared the CSP feature with the power spectral density (PSD) feature algorithm extracted by the fast Fourier transform and the input SVM classifier, respectively. The comparison of the test accuracy of 20 subjects under the two algorithms is shown in Figure 5. Experiments show that CSP features are significantly better than traditional features in intention recognition. It shows that the CSP intention feature extraction method can remove the EEG signal, which is not suitably related to intention, so that the classification effect can be optimized. The paired sample \(t\)-test showed that there were significant differences between CSP features and PSD features in SVM classification, t(19) = 23.851, \(p\) = 0.000, d = 0.99. The accuracy of the intention EEG CSP feature on the SVM (M = 90.93, SD = 9.80) of 20 subjects was significantly higher than that of the PSD feature (M = 31.18, SD = 8.95), as shown in Table 4.  
Figure 4: The recognition of different types of interaction intentions by the model. Figure 5: Recognition accuracy of the 20 subjects under different algorithms (units: %). In order to further intuitively show the effectiveness of the CSP feature matrix on the representation information of a multi-task intention signal, this paper reduces the high-dimensional space feature by the LDA dimensional-dimensional algorithm, and the feature separability is observed by scatter plot. LDA is a supervised dimension reduction algorithm that uses LDA to reduce the 12-dimensional CSP features and frequency point PSD features to a three-dimensional space for viewing. The EEG data of subjects 19, with low noise, good data quality, and a medium-level recognition rate of the three classifiers, were selected for visual display of the three types of features. As shown in Figure 6, the five colors represent the features constructed by the EEG signals of the five types of interaction. It can be seen that the data points of intention features extracted based on the CSP algorithm show cluster states, indicating that CSP features are better than the traditional CSP and have better spatial discrimination than the improved FBCSP, which further demonstrates the effectiveness of traditional CSP algorithm for intention feature extraction. Next, this paper analyzes the brain channel weights through the cospatial pattern projection matrix of each type of intention. First, extract the mixed space filter's energy value of 61 channels through a fast Fourier transform; then mapminmax is used to reduce the line vector to [0, 1]. Finally, the MATLAB topoplot function is used to visualize the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Paired Difference**} & & & \\ \cline{2-9}  & **Mean** & **Std.** & \begin{tabular}{c} **Standard Error of** \\ **the Mean** \\ \end{tabular} & \
\begin{tabular}{c} **95\% CI** \\ **Upper Limit** \\ \end{tabular} & **t** & **df** &
\begin{tabular}{c} **\(p\)** \\ **(Two-Tailed)** \\ \end{tabular} \\ \hline CSP-PSD & 59.75 & 11.20 & 2.51 & 54.50 & 69.99 & 23.85 & 19 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pared-samples \(t\)-test. Figure 6: Three-dimensional spatial visualization of subject 19 intention features: Training set: **(a)** PSD feature; **(b)** CSP feature; testing set: **(c)** PSD feature; **(d)** CSP feature. channel weights of the whole brain. Follow the above steps and observe intentions in spatial mode from a macro perspective. Table 5 shows the brain channel weight topography of the common spatial pattern projection matrix for all subjects. With a greater weight, indicating that the yellow region channel is more important for intention identification, and the blue area channel has a smaller weight. It was found that the EEG responses of subjects performing different interaction intentions are specific.