**1. Introduction**

World Health Organization (WHO) claims that over 1 billion people suffer types of disability worldwide [1]. Neurological disease is accepted as one of the reasons causing disability. For instance, stroke attacks more than 20 million people each year and causes 45% of the total to become permanent upper-body disabled [2], without

mentioning a significant number suffer from lower limb disability or temporary limited mobility. Although there have been several advanced therapies for neurological rehabilitation, the range of compatible users and the rehabilitation efficiency are still limited. For example, in 2017, Langhorne et al. [3] applied a very early rehabilitation trial (AREAT) among patients who suffered a stroke after 24 hours. However, in the inclusion criteria, patients must have at least a capacity to react with verbal commands. Patients with lock-in syndrome [4] would discover it tough to gain such interventions since injury risks might occur if patients could not timely express whether their bodies are suitable for the training intensity or not.

Brain-computer interface based motor imagery (MI-BCI) has the potential to unlock the potential interaction between these patients with their environment. Motor imagery (MI), imagining kinaesthetic movements of parts of the body [5], is widely suggested to utilize as a training method to improve physical capacities and rehabilitation outcomes. Articles claim that MI training could enhance physical performances in tennis [6], basketball [7], and Water polo [8]. Furthermore, this therapy could also positively affect functional network efficiency [9] and motor learning of locomotion [10]. Brain-computer interface (BCI), also called Brain-Machine Interface (BMI), is an advanced technology that plays an intermediary role in sharing information between a human's brain and external devices. With a non-invasive BCI device (wearable head cap with sensors attached for signal monitoring, rather than invasively inserting sensors into the skull), users could safely modulate their brain activity to interact with external devices, such as a robotic arm [11]. The interaction actively navigated by a human is called active BCI mode. This novel control strategy, combined with motor imagery [2, 12, 13], would widen the range of potential beneficiaries of assistive or rehabilitative robotics, such as diagnosed lockin patients. For example, using MI-BCI for exoskeleton navigation would help lock-in patients to have accessibility for walking assistance by mere imagination [14]. Combining with this principle of robotic therapy, such as residual cortical and subcortical neuronal group facilitation [15], a more significant number of patients can gain positive outcomes.

Dating back to one classical piece of research in 1993, one of the earliest MI-based BCI applications, called motor planning at that moment, was developed and reported at the University Technology of Graz [16]. Afterward, an increasing number of research groups began to focus on potential MI-BCI applications.
