## 4 Discussion  
For a long time, the classification of data with a small and imbalanced sample size has been a troublesome problem, which is especially true for fMRI data. In this study, in order to solve the problem of small and imbalanced fMRI data, we proposed a three-step method. First, the ICA method is used to reduce the dimension of the data, and then the SMOTE method is used to generate the samples of the minority class to balance the number of samples. Finally, the convolution-GRU model is used and achieves an accuracy score of 72.2%. In this section, we mainly discuss the following three points: (1) The reason why the convolution-GRU method is more suitable for the fMRI data; (2) The influence of MOT task and data division on classification performance; (3) The relationship between ICA results and episodic memory studies. In the classification of the public dataset used in this experiment, considering that too complex models may reduce the learning effect of small sample data, we used traditional classifiers such as LGR and SVM instead of CNN at first. However, as shown in the previous results, the traditional classifiers perform poorly on the fMRI data used in this study. It is because the feature dimension in this study is high and the nonlinearity is strong. SVM, LGR, LDA and other classifiers are essentially linear classifiers, which require a higher degree of linear separability of data. If they are used to classify high-dimensional nonlinear data, the effect will be reduced. Although the KNN method has no assumptions about the data and can be used for nonlinear classification, it is more sensitive to the size of the data and the degree of imbalance between the data, resulting a poor performance in classification. For these reasons, this study used ICA to reduce the dimension of the original data, and then used the SMOTE method to balance the number of two classes of samples. Finally, a convolution-GRU classifier is used to comprehensively learn the temporal and spatial features of the independent components of the data. While improving the classification performance of the majority class, the classification accuracy of the minority class is guaranteed. In addition, in this study, the FNC matrix is used as an input for classification, but since the FNC matrix does not contain temporal features, the classification performance is not as good as when independent components are used as training data. As can be seen from the classification results, although the classification performance of the convolution-GRU classifier is better compared to traditional classifiers, the performance of the minority class is not as good as that of the majority class. On the one hand, it is due to the small sample size of the data used in this study and the high degree of non-linearity of the data. The dataset we used in this study comes from only 15 subjects, and due to the task design, the sample sizes of two classes are imbalanced. The random oversampling method only gives the random copies of the minority class, which means it cannot give more information about the class. The basic opinion of SMOTE and ADASYN is to generate new samples based on the Euclidean distance, when the data size is adequate, the two methods have enough chance to mix the samples from the minority class and generate new ones. However, for the fMRI data in this study, the lack of data makes the problem of the minority class more serious. The generated data are easier to be influenced by the noise and individual bias. We think it is an important reason why there is a high difference between the classification of the majority and minority class. On the other hand, it is also related to the task. The data used in this study come from an episodic memory experiment. Using the words from word-scene pairs to do multi-target MOT and single-target MOT tasks ten times each, and only performing multi-target MOT tasks five times for the lure words, this caused an imbalance between the two types of data.