Robot-Assisted Rehabilitation System Based on SSVEP Brain-Computer Interface for Upper Extremity

Abstract

Traditional rehabilitation therapies have limited effect on the motor recovery for a tetraplegia patient. Brain-computer interface (BCI) systems allow patients to send commands or intents to control external devices without depending on the normal way of peripheral nerves and muscles. And hence it can provide an alternative control and communication method with a potential to replace, restore, even reinforce lost movement ability for individuals with neurological damages. In the study, we proposed a robot-assisted rehabilitation system for upper extremity based on non-invasive electroencephalogram (EEG) BCI, which enables the injured upper extremity to achieve motor function. Six participants conducted three speed modes of movement with a BCI-controlled robot. The steady-state visual evoked potentials (SSVEPs) was adopted to establish the BCI system. Experimental results of the healthy participants were analyzed to indicate the feasibility of a BCI-driven robot-assisted rehabilitation system, with an average accuracy of about 80 %. This study gives a preliminary evidence that the integrated robot-assisted rehabilitation system combined with SSVEP-based BCI will make future rehabilitation therapy more effective. I. INTRODUCTION HE Cerebral Palsy (CP), Amyotrophic Lateral Sclerosis (ALS), Spinal Cord Injury (SCI), and brain stroke can result in the loss of the movement function for individuals with partial or severe paralysis, which seriously affected quantity of daily life in their homes, workplaces, and communities. However, with effective rehabilitation therapies, impaired

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions

“…The advantages and disadvantages of each of them depend on the signal and the application. A simple and efficient ML algorithm, LDA, was among the best methods in terms of classification accuracy and ITR used in P300-based (ACC = 100% orthosis) ( Stan et al, 2015 ) (ACC = 94.3%) ( Duvinage et al, 2012 ), and SSVEP-based BCI systems selected in the SLR (ACC = 79%, ITR = 27.54 bpm-FES) ( Zhao et al, 2016 ), (ACC = 83.33%, ITR = 1.55 bpm -VR) ( Tidoni et al, 2017 ) (ACC = 82.22% -FES) ( Yao et al, 2011 ), (ACC = 80-96% -FES) ( Yao et al, 2012 ), (ACC = 82.30%, ITR = 27.4 bpm) ( Chu et al, 2018 ) (ACC = 92.4%) ( Gui et al, 2015 ), (ACC = 85% -FES) ( Ding-Guo and Ying, 2012 ). Moreover, classification Accuracy obtained with LDA in P300-based BCI is slightly higher than with SSVEP-based BCI.…”

To Reach the Unreachable: Exploring the Potential of VR Hand Redirection for Upper Limb Rehabilitation

“…Such motor impairments negatively affect their independent living, including performing activities of daily life [33,85]. Rehabilitation therapies are commonly adopted to help people with motor impairment regain strength and control over their impaired body parts and may help them fully or partially restore their motor ability [10]. Performing rehabilitation exercises in a hospital is a common step that many people with motor impairments (e.g., stroke survivors) take when following rehabilitation therapies.…”

Assistance Device Based on SSVEP-BCI Online to Control a 6-DOF Robotic Arm

“…Moreover, achieving success in these interventions requires a substantial commitment of time and effort. Numerous studies have demonstrated that the use of efficacious therapeutic methodologies can yield noteworthy enhancements in the patient’s state, notably in select instances of disorders of the nervous system [ 1 , 2 , 3 ].…”

“…The results of the study indicated an average accuracy of 65.89% in the BCI, based on the analysis of 10 subjects. The study conducted by the authors in reference [ 3 ] employs a similar paradigm to regulate a basic exoskeleton designed for arm rehabilitation. The system operates using three distinct control frequencies, namely, 12 Hz, 15 Hz, and 20 Hz.…”