## 1 Introduction  
Emotion is an important symbol of human intelligence; as such, an important intelligence symbol of artificial intelligence is that the machine can understand human emotions. As early as the 1980s, [PERSON], one of the founders of artificial intelligence, proposed that a machine without emotions is not intelligent. Recently, research on human emotion recognition has been applied in many fields such as entertainment [1], safe driving [2; 3], health care [4], social security [5], etc. [PERSON] et al. [6] believed that the emotional changes of the human were embodied in speech [7], facial expressions [8], body posture [9], the central nervous system, autonomic nerve physiological activities [10], etc. Thus,the study of human emotions through behavioral, facial, or physiological features has gradually become a focus of much attention. However, voice and facial expressions can be deliberately hidden by people on some social occasions. For this reason, researchers have tended to study human emotion through physiological signals such as electroencephalograms (EEG), electrooculography (EOG), temperature (TEM), blood volume pressure (BVP), electromyograms (EMG), and many other methods. Of all of these physiological signals, the EEG signal is of more interest to researchers as it comes directly from the human brain. Therefore, changes in EEG signals can directly reflect changes in human emotional states.  
In this study, we recognize human emotional states with EEG signals. Two important aspects must be ensured during the emotion recognition process: (1) EEG feature extraction and expression and (2) emotion classifiers construction. For the first aspect, most of the previously used methods have only focused on the time and frequency dimension, and rarely combine the spatial dimension. Therefore, how to integrate and present the spatial features of the EEG signal with the time and frequency features is one key problem. For the second aspect, the key problem lies in how to construct a classifier to automatically learn the changes from the EEG multidimensional features over time and classify the changes into different emotion states. Corresponding to these two aspects, we mainly undertook the following work in this study:  
* A new method is proposed to integrate the different EEG domain features. With the integration of multidimensional features, a sequence of two-dimensional images is constructed to express the variation in emotion.
* A hybrid deep learning neural network named CLRNN (Convolution al Networks (CNN) and Long-Short-Term-Memory Recurrent Neural Networks (RNN)) is built to undertake the recognitioNeurn of human emotion from the EEG multidimensional feature image sequences.
* Empirical research is conducted with the open-source database DEAP [11] using our method, and the results demonstrate significant improvements over current state-of-the-art approaches in this field.  
The rest of the paper is organized as follows: Related work is presented in Section 2. As data preparation, the methods of building EEG MFI and emotion labels are presented in Section 3. Next, we introduce the construction of CLRNN in Section 4. Section 5 describes the procedure of the experiment and reports the results. Finally, the conclusions and their discussion are detailed in Section 6.