The patients with brain diseases(e.g.,Stroke and Amyotrophic Lateral Sclerosis(ALS))are often affected by the injury of motor cortex,which causes a muscular weakness.For this reason,they require rehabilitation with co...The patients with brain diseases(e.g.,Stroke and Amyotrophic Lateral Sclerosis(ALS))are often affected by the injury of motor cortex,which causes a muscular weakness.For this reason,they require rehabilitation with continuous physiotherapy as these diseases can be eased within the initial stages of the symptoms.So far,the popular control system for robot-assisted rehabilitation devices is only of two types which consist of passive and active devices.However,if there is a control system that can directly detect the motor functions,it will induce neuroplasticity to facilitate early motor recovery.In this paper,the control system,which is a motor recovery system with the intent of rehabilitation,focuses on the hand organs and utilizes a brain-computer interface(BCI)technology.The final results depict that the brainwave detection for controlling pneumatic glove in real-time has an accuracy up to 82%.Moreover,the motor recovery system enables the feasibility of brainwave classification from the motor cortex with Artificial Neural Networks(ANN).The overall model performance reveals an accuracy up to 96.56%with sensitivity of 94.22%and specificity of 98.8%.Therefore,the proposed system increases the efficiency of the traditional device control system and tends to provide a better rehabilitation than the traditional physiotherapy alone.展开更多
基金the Declaration of Helsinki,and the protocol was approved by the Ethics Committee of Suranaree University of Technology(License EC-61-14 COA No.16/2561)the Thailand Research Fund through the RoyalGolden Jubilee Ph.D.Program(Grant No.PHD/0148/2557).
文摘The patients with brain diseases(e.g.,Stroke and Amyotrophic Lateral Sclerosis(ALS))are often affected by the injury of motor cortex,which causes a muscular weakness.For this reason,they require rehabilitation with continuous physiotherapy as these diseases can be eased within the initial stages of the symptoms.So far,the popular control system for robot-assisted rehabilitation devices is only of two types which consist of passive and active devices.However,if there is a control system that can directly detect the motor functions,it will induce neuroplasticity to facilitate early motor recovery.In this paper,the control system,which is a motor recovery system with the intent of rehabilitation,focuses on the hand organs and utilizes a brain-computer interface(BCI)technology.The final results depict that the brainwave detection for controlling pneumatic glove in real-time has an accuracy up to 82%.Moreover,the motor recovery system enables the feasibility of brainwave classification from the motor cortex with Artificial Neural Networks(ANN).The overall model performance reveals an accuracy up to 96.56%with sensitivity of 94.22%and specificity of 98.8%.Therefore,the proposed system increases the efficiency of the traditional device control system and tends to provide a better rehabilitation than the traditional physiotherapy alone.
