## 1 Introduction
In VR, the subject can perceive the imagined motor action, which could potentially activate mirror neurons that are also employed by mirror therapy for stroke rehabilitation [23]. The decoded brain oscillations are used to control a VR avatar which reproduces the imagined actions, most commonly between left and right-hand movement. This \"closed-loop\" feedback is presented in real time; therefore, the classification algorithm used should be fast, as well as accurate. Moreover, sensory feedback has been suggested to improve not only the performance [24], but also induce neuroplastic changes in post-stroke patients [9]. This is achieved mainly by involving a greater part of the sensorimotor system (e.g., visual, auditory, haptic and tactile feedback) and producing more distinguishable features [25]. During the process of identifying classification methods more suitable to the nature of EEG signals, many researchers were motivated to address the difficulties involved in classifying MI-EEG signals by employing deep-learning methods. Deep learning was selected due to its successful development in different fields, such as computer vision and speech analysis. Unlike conventional machine-learning approaches, deep learning can automatically identify individualized features from raw MI-EEG data using deep architecture, while eliminating the need for pre-processing and time-consuming feature extraction, since it can execute feature engineering by itself [26]. However, their disadvantages are also evident, since a large amount of data is required; in addition, the large number of hyperparameters which must be learned during training can increase the training time compared to other methods [27]. Another vital limitation of EEG-based BCIs is variability across subjects. It is found that the discriminative information of EEG signals varies based on the basic characteristics (e.g., demographics, prior BCI experience) and psychological states (e.g., motivation, confidence and frustration) of the BCI users [28]. This phenomenon, combined with the need for minimizing the training and calibration time of the BCI system, leads to the use of transfer learning [29, 30]. Transfer learning describes the procedure of using data to solve one problem and applying it to a different but related problem. Transfer functions are employed so that the classification models can be adapted from the source domain to different target domains. However, the effectiveness of transfer learning strongly depends on how well related the two subject domains are [31]. Nonetheless, we cannot neglect the fact that stroke patients suffer from individualized lesions, which might evoke different EEG patterns. Therefore, current efforts might be directed towards adaptive models to tailor the individual use of the system [32]. In other words, the difficulty in reducing individual differences is to consider individual attributes comprehensively, and then to select some of the attributes that are effective for the system. Although a lot of research has been devoted to identifying and comparing the best algorithm in offline BCI systems with healthy subjects, detailed information on which classifiers lead to the most accurate prediction of motor imagery is still missing in online BCI systems with post-stroke patients. It is still unclear whether the cortical lesions of post-stroke patients evoke discriminable EEG patterns for different body parts. Our perspective is that if both traditional and novel classification algorithms induce insufficient accuracy, then the features generated by the EEG MI signals of post-stroke patients might not be distinguishable. Overall, it is widely agreed that recovery may be promoted through contingent activation of efferent and afferent pathways. A good level of BCI accuracy is, thus, a prerequisite (otherwise, there is no adequate contingency between the BCI command \"efference\" and the feedback).