## 1 Introduction  
Electromyography (EMG) is a biological current generated by the contraction of muscles on the surface of the human body. The nervous system controls muscle activity (contraction or relaxation), and different muscle fiber motor units on the surface of the skin produce different signals at the same time. The electromyographic signals collected on the skin surface are called surface electromyographic signals (sEMG) [1]. Measuring the surface electromyographic signal has the advantage of being non-invasive [2] and it has been a popular method used for testing in medicine [3; 4; 5]. Furthermore, the sEMG signal is generally generated 30-150 ms ahead of the limb movement, and the movement can be judged in advance; therefore, the next action can be predicted by sEMG, and consequently, the sEMG prediction model has emerged. The surface electromyographic signal is one of the most used biological signals for motion intention prediction [6]. Hand gesture recognition based on sEMG signals is one of the main paradigms for prosthetic and rehabilitation device control [7]. Upper limb amputation brings a great inconvenience to trans-radial amputees' lives and myoelectric prostheses can help them to improve their life quality. Several electrodes are used to record myoelectric signals. Then, the motion intentions can be recognized by pattern recognition algorithms [8]. In the past decades, sEMG-based hand motion intention recognition has primarily been researched as a classification task [9; 10; 11]. Many limitations exist in real circumstances that hinder the classification algorithm research of myoelectric prostheses, for example, few amputated volunteers can be recruited in the experiments resulting in limited data. In addition, the collected signals can further suffer from signal quality deterioration due to muscular atrophy and noise pollution, etc. These problems can lead to the inaccurate recognition of motion intentions and, thus, result in the limited capabilities and adoption of myoelectric prostheses [12]. In the recent years, transfer learning technology has evolved as a novel learning framework for transferring information from one interest domain to another [13], and there have been several applications of transfer learning in myoelectric control. Most of these applications focus on solving cross-subject problems and numerous researchers have been dedicated to constructing more robust recognition algorithms by utilizing historical data from source subjects. [PERSON], [PERSON] et al. [14] observed that sEMG signals varied greatly between individuals and that models trained on diverse people are mainly subject specified; however, they demonstrated that a pre-trained model was effective in reducing the amount of time needed for an individual to become proficient with a prosthesis. [PERSON], [PERSON] et al. [15] split myoelectric signals into two independent parts named the motion-dependent part and the user-dependent part, such that models could be reused by rapidly learning only the user-dependent part for a new user. [PERSON], [PERSON] et al. [16] proposed to concatenate the source and target data and showed various classification algorithms on it. [PERSON], [PERSON] et al. [17] proposed a user-adaptive model based on CNN. They used deep learning to resolve a sEMG-based gesture recognition task for the first time. This model outperformed the support vector machine (SVM) in terms of its classification accuracy and robustness. [PERSON], [PERSON] et al. [18] presented an inter-participant transfer learning framework based on a progressive neural network (PNN) to improve cross-subject recognition accuracy, in which each subject was considered as a source task. [PERSON], [PERSON] et al. [19] proposed a modified Lenet combined with a pretrain mechanism to improve the recognition performance on amputees. These studies proved the possibility of using transfer learning in the field of myoelectric control, especially on the problem of low recognition accuracy when a model was applied across subjects.