Development of an Application That Implements a Brain-Computer Interface to an Upper-Limb Motor Assisted Robot to Facilitate Active Exercise in Patients: A Feasibility Study  
[PERSON]  
1 Department of Rehabilitation, Graduate School of Health Sciences, Saitama Prefectural University, 820 Sannomiya, Koshigaya 343-8540, Saitama, Japan; [EMAIL_ADDRESS]  
[PERSON]  
1 Department of Rehabilitation, Graduate School of Health Sciences, Saitama Prefectural University, 820 Sannomiya, Koshigaya 343-8540, Saitama, Japan; [EMAIL_ADDRESS]  
Footnote 1: email: {T.,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,  
therapist [6; 7], and monitoring robots that allow remote therapist-patient interaction allow patients to train under therapist supervision at home [8]. In addition, robots that can encourage independent practice have been developed to enable effective training regardless of location and time [9]. Techniques have also been developed to assess joint motion with an accuracy comparable to that of humans and evaluate the effectiveness of these robotic exercises [10; 11]. In rehabilitation robots, brain-computer interface (BCI) technology is used to transmit the patient's active brain activity to the robot. A BCI is a system that records brain signals, decodes them, and converts them into effector movements. This technology operates robots based on changes in the brain waves and cerebral blood flow [12]. A biofeedback approach combining BCI with an exoskeletal robot as an effector and electrical stimulation has been reported to improve the Fugl-Meyer Assessment of upper-extremity (FMA-UE) function scores compared with conventional robotic therapy [13]. Motor imagery (MI), which activates certain brain regions when performing movements [14], has been reported to exhibit a phenomenon in which the electrical potential of the brain rapidly decreases compared with its resting state, known as event-related desynchronization (ERD) [15]. Therefore, MI-related brain waves are sometimes utilized as biomarkers in BCIs, leading to the development of BCI robots that provide feedback, such as movement assistance and electrical stimulation. The diccephalus robotic system (DiC), a training robot for the upper-extremity joint range of motion developed at Saitama Prefectural University, can partially reproduce a therapist-assisted upper-limb movement [16]. Patients can practice repetitive upper-limb motion exercises with almost accurate trajectories through assisted movements reproduced by the robot, thus recovering upper-limb functions. However, BCIs are currently unable to determine whether the patient is actively performing upper-limb exercises; therefore, a BCI that can determine the patient's active participation to enhance motor function recovery is needed. During rehabilitation, neurofeedback enables active motor function training, even in patients with poor motor skills due to paralysis. The sequence of joint movements and sensory feedback in accordance with the motor intention is called a closed loop, and this repetitive stimulation promotes recovery from paralysis [17]. In other words, when a robot is activated by a BCI using the brain waves of a patient performing MI, even a patient with severe paralysis can perform active-assistive upper-limb movements. By combining the feedback method with brain wave driving, patients can perform the closed-loop exercise and repeat it, which is expected to accelerate recovery more than the conventional rehabilitation method [18]. Patients with severe paralysis of the upper extremities after a stroke are believed to have decreased brain function due to disused plasticity [19]. However, it may be operable if the ERD in MI can be detected, even in a state of brain dysfunction [20]. Therefore, we aimed to verify whether ERD can be detected in patients with severe paralysis of the upper extremities in the recovery phase when they perform MI while watching a movie about arm movements.