## Materials and Methods
### Data Analysis
#### 2.3.4 Data Oversampling  
As introduced in the procedure, the MOT tasks are divided into three categories, namely: targ_easy, targ_hard, and lure_hard. In each scan, there are 10 targ_easy tasks, 10 targ_hard tasks, and 5 lure_hard tasks. Considering that the most significant difference between the three tasks is whether the word is from the word-scene pairs, so the data are divided into two categories, namely the target class and the lure class. Obviously, there is a serious imbalance in the number of samples of the two sets of data. As the majority class, the size of target class is three times that of the minority class. If the data are directly input into the classifier, it will make the classifier learn too much of the majority class data. If the data are directly input into the classifier, the test results will also be biased to the majority class, which is very disadvantageous for the training of the classifier. In order to balance the number of  
Figure 3: The results of ICA of fMRI and removal of noise and artifacts. After performing ICA, a total of 39 independent components that do not overlap in space are obtained. After manually removing 14 noises and artifacts, the 25 independent components shown in the figure are finally obtained and used as input to the classifiers. training samples of the two categories, this study used Synthetic Minority Oversampling Technique (SMOTE) to balance the data set. The basic idea of the SMOTE method is to use samples from the minority class to synthesize new samples and add them to the data set, so that the number of samples is balanced. The process of SMOTE algorithm is as follows:  
1. First define a feature space and project all samples as points in this feature space. Then determine the sampling ratio according to the sample ratio of the majority class and the minority class. 2. For each minority sample \((x,y)\), find \(K\) samples closest to this sample according to the Euclidean distance, randomly select one \((x_{n},y_{n})\) from the \(K\) samples, and construct a new one as follows: \[(x_{new},y_{new})=(x,y)+rand(0,1)\cdot((x_{n}-x),(y_{n}-y))\] (4) That is, randomly select a point on the line between the sample \((x,y)\) and the nearest neighbor sample \((x_{n},y_{n})\) as the new minority sample. 3. Repeat the above steps until the sample size is balanced. After processed by the SMOTE method, the number of samples of the two classes is balanced and can be used to train the classifier in the subsequent steps. It should be noted that, in order to ensure the objectivity of the experimental method, this study only performs the SMOTE method on the training set, and the test set remains unchanged. In addition to SMOTE, there are two other data oversampling methods that are also commonly used: the random oversampling method and the adaptive synthetic (ADASYN) method. The random oversampling method is easy to implement. Its principle is to randomly sample the minority samples to increase the number of minority samples. The ADASYN method is similar to SMOTE, and its basic process is as follows:  
1. Calculate the degree of imbalance between the two classes: \[p=\frac{n_{s}}{n_{l}}\] (5) where \(n_{s}\) is the sample number of the minority class and \(n_{l}\) is the sample number of the majority class. When p is less than the tolerance threshold, the oversampling process begins. 2. Calculate the number of samples to be synthesized: \[M=(n_{l}-n_{s})\cdot\beta\] (6) \(\beta\in[0,1]\) is the generation ratio, when \(\beta\) is 1, the number of the majority class and the balanced minority class are the same. 3. For each sample of the minority class, the K nearest neighbor is calculated according to the calculated Euclidean distance, and the ratio of the majority class \(r_{i}\) is calculated. Obviously, \(r_{i}\in[0,1]\). 4. Standardize \(r_{i}\): \[\hat{r_{i}}=\frac{r_{i}}{\sum\limits_{k=1}^{n_{s}}r_{k}}\] (7)
5. Calculate the number of new samples that need to be generated for each minority sample: \[g_{i}=\hat{r_{i}}\cdot M\] (8)
6.