## 1 Introduction  
Walking is not only one of the most fundamental activities that enables a person to be an active member of our society, but it is also the key to maintain the health condition [1]. For people who have completely lost their walking ability due to spinal cord injury, therefore, it is mandatory to stand up and perform walking motions frequently with the help of assistive devices (e.g., a harness system, a robotic rehabilitation treatment system, etc. [2]) to maintain the health condition. In this aspect, powered exoskeletons provide the complete paraplegic with the possibility to walk on the real ground, which is meaningful for rehabilitation of complete paraplegics, as well as their mobility. Powered exoskeletons have been developed by many researchers in recent years, and several robots have been commercialized [3]. Among many applications of the wearable robots, ones for assisting people with complete paraplegia may be the most advanced. Wheelchairs can improve the mobility of patients with complete paralyses, but they cannot provide standing postures and bipedal walking, which are important for healthcare. Many studies have shown that bipedal walking training can increase life satisfaction [1,2,4,5]. ReWalk, a commercial robot by ReWalk Robotics [6], is being distributed to rehabilitation hospitals around the world. In addition, Ekso [7] from Ekso Bionics, Indego [8] from Parker Hannifin corporation and Rex [9] from Rex Bionics are commercially available robots for disabled and paralyzed people. Many other robotic exoskeletons are being developed and commercialized [10,11]. The mechanism and control of powered exoskeletons have been studied mainly in terms of efficient force transmission generating a natural gait pattern. Through intensive studies, this has reached the completion stage to some extent. For more improved usability of powered exoskeletons for people with complete paralyses, it is important to accomplish not only the transmission of the actuation force, but also the immediate recognition of the user intention. At the same time, the user must be informed immediately if any event (e.g., mode change, fault detection, battery discharge, etc.) occurs in the robot system. Therefore, it is mandatory to design a user-friendly, comfortable, convenient, yet reliable user-interface (UI). Various intention detection methods have been studied. One of the promising methods is to measure the physical contact force, such as a ground reaction force (GRF), measuring the reaction force between the ground and the user's foot [12; 13]. The GRF can be a real-time indicator of whether or not the feet are on the ground, but the intention cannot be detected in advance. In general, when the GRF is measured below a certain threshold value, the foot leaves the ground and is detected as a swing phase. However, for the case of complete paraplegics, it is impossible to generate the swing phase due to the complete loss of motor function of the lower limb. Human motion intent can also be detected using biomedical signals such as electromyography (EMG) of the leg muscles [14; 15; 16; 17; 18; 19]. The EMG method is not suitable for patients who have completely lost their motor and sensor functions because they cannot sense the actual motion or contraction of the muscles. The intention of movement is transferred by electrical signals induced by the brain's neurons, and a control method using a brain-machine interface (BMI) has recently been widely studied. An invasive BMI method has been used in monkeys in many studies to measure and analyze the brain signals of body movements [20; 21]. However, the invasive BMI methods are too risky and cannot be applied to daily-life applications [10; 22]. As a non-invasive BMI application, scalp electroencephalographic (EEG) has been utilized also [10]. For example, many applications used the EEG-based BMI to detect intention and control the assistive devices, such as orthotic devices and wheelchairs [11; 23; 24; 25]. [PERSON] et al.