Investigating The Detection of Intention Signal During Different Exercise Protocols in Robot-Assisted Hand Movement of Stroke Patients and Healthy Subjects Using EEG-BCI System

Abstract

Improving the hand motor skills in post-stroke patients through rehabilitation based on movement intention derived signals from the brain in conjunction with robot-assistive technologies are explored. The experimental work is conducted using Electroencephalogram based Brain-Computer Interface (EEG-BCI) system and the AMADEO hand rehabilitation robotic device. Two protocols using visual-cues and then using a 2-Dimensional (2D) interactive game is presented on a computer screen to healthy subjects as well as post-stroke patients performing the hand movements. The movement intention signals during hand movement are detected through the Support Vector Machine (SVM) classifier. The intent signals produced at six distinct electrodes are investigated to determine electrodes contributing most to the SVM classifier's performance. Overall, the game protocol shows better classification results for both healthy and stroke patients compared to the visual-cues protocol. FC3 is found to be the most consistent electrode site for the detection of the motor intention of the hand for both protocols. In the experimental work, average classification accuracy for the visual-cues protocol of 67.56% for healthy subjects and 56.24% for stroke patients were obtained. For the game protocol, the classifier accuracy produced for healthy participants was 79.7% and for the post-stroke patients was 66.64%. The results confirm that the intention signal is more pronounced during more engaging activities, such as playing games, for both healthy and stroke subjects. Therefore, the effectiveness of rehabilitation therapy for post-stroke patients could be significantly enhanced using interactive and engaging exercise protocols.

A Systematic Review of Virtual Reality and Robot Therapy as Recent Rehabilitation Technologies Using EEG-Brain–Computer Interface Based on Movement-Related Cortical Potentials

“…Several articles performed tests for both healthy subjects and patients. In [ 64 ], four SCI patients and three healthy subjects were to test clinical safety and validate the technology; [ 52 ] had four healthy subjects and two patients; and [ 49 ] had only one patient and two healthy subjects. All of these studies used different procedures for the two groups.…”

“…Furthermore, in [ 15 , 52 , 62 ], the researchers used the customized upper-limb exoskeleton AMADEO robotic device manufactured by Tyro motion GmbH, Bahnhofgürtel 59, 8020 Graz, Austria. AMADEO is a distal upper-limb motor recovery end-effector robot-assistive device for post-stroke patients.…”

“…The AMADEO robot device’s joints have 5 DoF and present four modes of instruction, including passive training, passive training with biofeedback, assistive training together, and active 2D training games [ 62 ]. In [ 52 ], four healthy subjects and two post-stroke patients used the AMADEO robotic device to detect intention signals during the visual cues and AMADEO game training protocols. The effectiveness of the two training protocols was more evident during the AMADEO game training protocol for healthy subjects and patients.…”

“…As with other performance measures, accuracy can be defined as the ratio between correctly observed and wrongly observed movements to the total number of observed movements. Four studies among the identified studies used accuracy, in percentages, as the performance measure [ 50 , 52 , 57 , 63 , 75 ].…”

Effect of Robot-Assisted Training on EEG-Derived Movement-Related Cortical Potentials for Post-Stroke Rehabilitation–A Case Series Study

“…It has five degrees of freedom that allow passive, assistive as well as active movement (with the help of 2D interactive games) of fingers and thumb. Many studies have used AMADEO for post-stroke rehabilitation [45]- [47]. For instance, Xianwei et al [46], [47] used AMADEO for fine finger motor recovery of post-stroke patients.…”