## 3 Results
### Classification of Using FNC Data  
In addition to the ICA of the original data, we used the AAL90 template to obtain the FNC matrix of the subjects when performing the task. In order to compare with the classification results using independent component time series data for classification, we also used the FNC matrix as input to train the classifier. It should be noted that, because the FNC matrix we calculated is static data, not time series, no model containing GRU is selected for training. As shown in Table 4 and Figure 6, when using the CNN model for classification, the highest classification accuracy rate is achieved, reaching \(71.4\pm 3.6\%\), but the recall rate of the minority class is poor, only \(11.1\pm 4.7\%\), very unstable. LDA and KNN in traditional classifiers perform badly. These two classifiers are too biased to divide the data into the minority class, which makes the classification performance of the majority class worse. \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Weighted F1** \\ \hline LDA & 0.6061 \\ KNN & 0.4801 \\ LGR & 0.5987 \\ SVM & 0.6318 \\ CNN & 0.6582 \\ GRU & 0.6555 \\ C-sGRU & 0.6624 \\ C-GRU & **0.6827** \\ \hline \hline \end{tabular} Since we want to evaluate the comprehensive performance of the classifier, here we directly use the average of the precision and recall of the classifier to calculate the weighted F1 score. It can be seen from the table that the deep learning model still has great advantages over the traditional model.  
\end{table} 
Table 2: Weighted F1 of the methods in IC classification. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **ACC** & **LP** & **LR** & **SP** & **SR** \\ \hline RO & 0.7046 (0.0489) & 0.7977 (0.0166) & 0.8450 (0.0668) & 0.1939 (0.0831) & 0.1435 (0.0841) \\ SMOTE & **0.7222 (0.0290)** & **0.8127 (0.0146)** & 0.8484 (0.0372) & **0.2670 (0.0609)** & **0.2182 (0.0640)** \\ ADASYN & 0.7157 (0.0259) & 0.8031 (0.0124) & **0.8542 (0.0297)** & 0.2157 (0.0657) & 0.1617 (0.0613) \\ \hline \hline \end{tabular}  
* RO: Random Oversampling. Random oversampling, SMOTE, and ADASYN are common data balancing methods. In this study, these three methods are used to balance the data, and the classification performance of convolution-GRU is compared. It can be seen that the SMOTE method performs better than the other two methods in the accuracy of the minority class. It should be noted that all the three balancing methods are used in default parameters.  
\end{table} 
Table 3: Performance of different oversampling strategy. The performance of LGR and SVM is more balanced. Although the accuracy and performance of most classes are not as good as the CNN model, they perform better in a few classes. Similarly, here also gives the weighted F1 value of each classifier when using FNC data for classification, as shown in Table 5. The deep learning model represented by CNN still maintains its advantage, reaching 0.6558, while the weighted F1 value of the KNN model is only 0.2716, which is the worst performance. In general, when using FNC as the input training classifier for classification, the three methods, LGR, SVM, and CNN, all have good performance in terms of accuracy, but the CNN method is too