# A Study on Sensitive Bands of EEG Data under Different Mental Workloads
## 4 Significance Analysis of Mental Workload and Feature Based on SVM Classifier
### Application of SVM Classifier to EEG Signals  
Considering the individual differences, a load classification model for each subject is established separately. The following is an example of subject 22 to illustrate the construction of the classification network. The classification data set information after feature extraction and feature selection are shown in Table 3. The data volume and data dimension of different loads, training set, cross-validation set, and test set are given in the table. The dataset of subject 22 obtained in Section 4.2 can be expressed as A\({}_{1}\), A\({}_{2}\),..., A\({}_{1749}\). Each dataset has 120 dimensions, where the low load tag is 0, the moderate load tag is 1, and the high load tag is 2. Since the sample size of each load level is 583, the effective number of samples of this model is 1749. The input information of all feature data when using the SVM classifier is  
\[\left[\mathbf{X}_{d1}\right]_{\text{mod1}}=\left[ \begin{array}{c}\left[A_{1} \right]^{1},\ldots,\left[A_{1}\right]^{120}\\ \left[A_{2}\right]^{1},\ldots,\left[A_{2}\right]^{120}\\ \ldots\\ \left[A_{1749}\right]^{1},\ldots,\left[A_{1749}\right]^{120}\end{array} \right]\]  
\[\left[\mathbf{Y}_{d1}\right]_{\text{mod1}}=\left(0^{1},\ldots,0^{583},1^{1}, \ldots,1^{583},2^{1},\ldots,2^{583}\right)^{T}\]  
The input information of the \(\beta\) band feature data when using the SVM classifier is  
\[\left[\mathbf{X}_{d1}\right]_{\text{mod1}}=\left[ \begin{array}{c}\left[A_{1 }\right]^{31},\ldots,\left[A_{1}\right]^{60}\\ \left[A_{2}\right]^{31},\ldots,\left[A_{2}\right]^{60}\\ \ldots\\ \left[A_{1749}\right]^{31},\ldots,\left[A_{1749}\right]^{60}\end{array} \right]\]  
\[\left[\mathbf{Y}_{d1}\right]_{\text{mod1}}=\left(0^{1},\ldots,0^{583},1^{1}, \ldots,1^{583},2^{1},\ldots,2^{583}\right)^{T}\]  
The training set and the test set are randomly divided by 70%:30%. After the classification network is adjusted to the appropriate parameters, the network training is completed. The SVM model has two very important parameters \(C\) and \(\gamma\). Where \(C\) is the penalty factor, which is the tolerance for the error. The higher the \(C\) value is, smaller error can be allowed, and the over-fitting occurs more easily. The smaller the \(C\) value is, the easier under-fitting occurs. If the \(C\) value is too large or too small, the generalization ability is poor. \(\gamma\) implicitly determines the distribution of data after mapping to a new feature space. The larger the \(\gamma\) is, the more vectors there are. The number of support vectors affects the speed of training and prediction. So the two critical parameters of the classification network are the \(C\) and \(\gamma\) value. Here, the parameters are adjusted according to the accuracy of the cross-validation set, using k-fold cross-validation. We use 10-fold cross-validation since it is the most widely used. The average of 10 results is used as an indicator, and a single estimation is finally obtained. This ensures that each subsample participates in the training and test, reducing generalization errors and preventing overfitting. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Data** & **Low Load** & **Moderate Load** & **High Load** & **Training Set** & **Cross-Validation Set** & **Test Set** \\ \hline After Feature & number & 583 & 583 & 583 & 1224 & 122 & 525 \\ Extraction & dimension & 120 & 120 & 120 & 120 & 120 & 120 \\ \hline After Feature & number & 583 & 583 & 583 & 1224 & 122 & 525 \\ Selection & dimension & 30 & 30 & 30 & 30 & 30 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification data set after feature extraction and feature selection. Taking the subject 22 as an example, the accuracy of the cross-validation set under different parameters is shown in Figure 11. It can be seen from the figure that when \(C=5000\) and \(\gamma=0.00003\), the accuracy of the cross-validation set is the highest. The classification result confusion matrix is used to reflect the quality of the classification results.