## 1 Introduction  
Worldwide, stroke is a leading cause of adult long-term disability [1]. Growing evidence supports that chronic stroke patients maintain brain plasticity, meaning that there is still potential for additional recovery of impaired limbs [2]. Consequently, various motor rehabilitation techniques have been developed, including motor training [3], mirror therapy [4], motor imagery (MI) [5] and action observation (AO) [6]. Presently, there is increasing evidence that motor imagery practice combined with brain-computer interfaces (MI-BCI's) in a closed-loop could promote long-lasting improvements in motor function in chronic stroke patients [7; 8; 9]. Specifically, BCI's can act as an alternative non-muscular communication channel between the user's brain and a computer system for motor rehabilitation. MI is the cognitive process of imagining the movement of a body part without actually moving it [10]. Through MI, stroke rehabilitation effectively promotes structural and functional reorganization [11]. This is achieved due to the repeated recruitment of motor-neuron circuits which repair connections between damaged neurons through neural plasticity, and eventually improve motor dysfunction [12]. The neurophysiological mechanisms underlying the MI practice are reflected in sensorimotor rhythms (SMR), recorded through electroencephalography (EEG) [13]. This type of rhythmic oscillation refers to the organized neural activity modulated by the MI and recorded over the sensorimotor cortex as decreases in the Alpha (8-12 Hz, also known as Mu rhythm) and Beta (13-26 Hz) frequency bands. When activity in a frequency band increases in response to stimuli, it is called event-related synchronization (ERS), while a decrease is called event-related desynchronization (ERD) [14]. The translation of the brain signals to the output of the BCI, as a neurofeedback, is accomplished through the distinct features aroused by the MI of different limbs. Traditionally, the classification involves a machine-learning algorithm [15]. However, EEG signals pose processing challenges, since they exhibit low signal-to-noise ratio (SNR) and are prone to signal artifacts and external noise. Therefore, pre-processing is mandatory in order to remove those signals that are unrelated to the brain (i.e., heartbeats, eye blinking, tongue and muscle movements, electronic equipment and environmental noise). This notwithstanding, combined with the high dimensionality of EEG signals, interpretation, and classification of brain signals is a difficult task, with many of the approaches utilized suffering from poor classification accuracy. In addition, the training of the subject and the BCI system is usually prolonged, as most of the classification methods require a significant amount of data to predict the MI label accurately [16]. However, motivation and attention have an important influence on the emergence of distinguishable features for various MI tasks and the stability of EEG patterns. A monotonous MI-based BCI practice frequently affects the engagement and concentration levels of the subjects, leading to the poor translation of the brain signals by the computer interface and a decline in therapy effectiveness [17]. Consistent with this viewpoint, several studies have reported that the feedback modalities of BCI systems create a more immersive and motivating environment, which increases the embodiment of the user and, thus, provides more robust EEG features [18]. Therefore, machine-learning algorithms should adapt to non-stationary brain signals, while human learning approaches should assist in the production of more consistent EEG patterns for the user. The feedback modalities used for BCI motor rehabilitation include: non-embodied simple two-dimensional graphics on a screen [19], embodied avatar representation of the patient on a screen or with virtual reality (VR) [20], functional electrical stimulation (FES) [21] or robotic exoskeletal movement [22].