## 1 Introduction  
Advances in myoelectric interfaces have the potential to revolutionise the use of wearable prosthetic devices as artificial substitutes for missing limbs. Active hand and arm prostheses are usually controlled by electromyography (EMG) signals. EMG signals can be recorded from remnant muscles using either invasive or non-invasive electrodes. In most commercially available upper limb prostheses, EMG is used for on-off control, which allows the prosthesis to move bi-directionally with constant velocity [1]. With the addition of proportional control, the velocity of a prosthetic function is proportional to the intensity of the EMG signal [2]. These clinical control schemes are based on the direct association (direct control) of EMG signals to the actuation of degrees of freedom (DoF) [1] and therefore require at least two independent EMG channels to drive each DoF. To overcome this limitation, control schemes based on pattern recognition (PR) [3; 4; 5; 6] have been used to decode several prosthesis functions using supervised learning. A number of algorithms have been tested for EMG classification, including artificial neural networks (ANN) [7; 8; 9; 10; 11], log linearized Gaussian mixture networks (LLGMN) [12; 13; 14; 15], Fuzzy mean max NN [16], radial basis function [17], hidden Markov model [18], Bayesian network [16; 19], random forest [20], k-nearest neighbors (KNN) [21; 22], support vector machine (SVM) [23], and linear discriminant analysis (LDA) [24; 25]. Some of these algorithms achieved classification accuracies above 95% for up to 10 classes when applied to temporal and frequency EMG features [26]. PR-based approaches are promising compared to conventional methods. However, their clinical usability is still in its infancy and thus the natural control of prostheses is still limited to a few basic movements [27; 28]. This calls for better machine learning methods to improve usability of PR schemes. The sophisticated deep learning algorithms impact several applied fields, such as computer vision [29] and speech recognition [30]. In addition to classical machine learning methods, deep learning algorithms, such as autoencoders (AE) and convolutional neural networks (CNN), have been used in biomedical signal applications [31], such as in electroencephalography (EEG) [32; 33; 34] and electrocardiography (ECG) [35; 36; 37; 38]. Despite some applications of EMG processing [39; 40; 41; 42], AE-based methods have not been extensively applied in myoelectric control. Several studies evaluated the CNN in myoelectric control; [PERSON] and [PERSON] [43] decoded movement intention from EMG using a CNN that outperformed a SVM classifier. [PERSON] et al. [28] proposed a deep networks-based algorithm for classification of surface EMG (sEMG) associated to hand movements in the publicly available Ninapro database of intact-limb and amputee subjects [44]. This technique provided comparable performance with respect to KNN, SVM, random forest, and LDA. Other studies [27; 45; 46; 47; 48; 49] evaluated deep learning methods and concluded that these methods either performed better or comparable to classical machine learning algorithms. Most of these studies were performed with datasets recorded in a single session, limiting the usefulness of deep networks. Hence, performance of deep networks over multiple sessions needs to be assessed with both sEMG and intramuscular (iEMG). Whereas myoelectric control is most commonly applied with sEMG, iEMG has also been proposed as an approach to overcome some of the limitations of non-invasive systems [50]. For example, [PERSON] et al. [3] showed that the classification accuracy of a myoelectric control system with combined surface and intramuscular EMG was better than sEMG alone. Other studies [51; 52; 53; 54; 55] compared the individual performance of sEMG and iEMG for classification of different hand and wrist movements and generally found similar performance (no significant difference).