## 1 Introduction  
Functional magnetic resonance imaging (fMRI) is a very effective non-invasive technique to study brain functions. The commonly mentioned fMRI mainly refers to BOLD-fMRI, which relies on the difference in the magnetization vector between oxyhemoglobin and deoxyhemoglobin to generate the fMRI signal, thereby obtaining changes in cerebral hemodynamics [1]. Due to its excellent non-invasive and high temporal and spatial resolution, fMRI method has become the most widely used method in the brain function researches. fMRI can accurately and reliably locate the cortical area of specific brain activity, which can be used to diagnose brain diseases. Another merit of fMRI is that it can track signal changes in real time and obtain time series of brain activities [2]. Therefore, a large number of brain science researchers began to study fMRI and introduced it to neuroscience field. Through magnetic resonance imaging (MRI), researchers can get high-resolution anatomical images. At the same time, the function phase obtained by fMRI technology includes the signal changes over a period of time. After the brain function signals are obtained, the GLM model [3] can be used to calculate the brain activation levels of the subjects, including the individual level and the group level. The combination of anatomical images and function phases can reveal some patterns of human brain activities, such as the activation of the brain area [4], the degree of lateralization [5], etc. However, most of the researchers focus on the obtained static images, and then get some qualitative patterns. An adult brain has tens of billions of cells. The number of the obtained voxels will be different according to the fMRI scanning interval. Taking the 3 mm scanning thickness as an example, the scanned four-dimensional data also contain about 270,000 voxels. If we only use activation maps, the temporal activity patterns of brain regions will be ignored, which cannot be accurately described by activation maps alone. However, if the activation intensity value of each voxel is manually calculated and observed, it is often difficult to achieve due to too much calculation and too many restricted conditions. The application of machine learning algorithms alleviates the problems above. With the help of machine learning algorithms, the problem of insufficient computing power for manual calculations has been partly solved. Support vector machines (SVM) [6; 7], random forest (RF) [8], logistic regression (LGR) [9] and other classifiers are widely used in the classification of fMRI data. In addition to classification, some machine learning methods have also been applied to other aspects of fMRI analysis. In the past studies, sparse coding [10; 11] proved to be a good tool to focus on the activity of certain brain networks during tasks. Through the training of a large amount of data, the researchers obtained a lot of activity patterns of different brain regions under specific tasks. Even so, for hundreds of thousands of voxel signals in multiple brain regions, manual feature extraction is a very complicated work, although various feature extraction methods have been proposed [12; 13]. Due to the complexity of the working mechanism of the human brain, it is difficult to say which extraction method is more appropriate for specific fMRI tasks. Therefore, as a supplement to various feature extraction methods, the independent component analysis (ICA) method was applied to the feature extraction of fMRI [14; 15]. ICA assumes that the obtained fMRI signal is the result of superposition of multiple independent signal sources (spatially independent components). By blindly separating the fMRI signal, a spatially independent component map is obtained, which enhances the spatial connection of features. The introduction of complex learning networks such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) [16; 17] further increases the ability of fMRI data feature extraction and processing.