BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients

Abstract

Brain computer interface (BCI) controlled assistive robotic systems have been developed with increasing success with the aim to rehabilitation of patients after brain injury [1] to increase independence and quality of life. While such systems may use surgically implanted invasive sensors, non-invasive alternatives can be better suited due to ease of use, reduced cost, improvements in accuracy and reliability with the advancement of the technology and practicality of use. The consumer grade BCI devices often capable of integrating multiple types of signals, including Electroencephalogram (EEG) and Electromyogram (EMG) signals, as well as basic motion based signals such as gyroscopic data. This paper summarises the development of a BCI controlled robotic system using a non-invasive BCI headset "Muse" [2] and an open source robotic arm, U-Arm [2], to accomplish tasks related to rehabilitation, such as access to resources, adaptability or home use. The resulting system used a combination of EMG and gyroscopic sensor readings to control the arm, which could perform a number of different tasks such as picking/placing objects or assist users in eating. Preliminary work was carried out to capture event driven EEG signals as alternative inputs to the controller. To avoid risks of injury while the device is being used in clinical settings, appropriate measures were incorporated into the software control of the arm. The project was a success in collaboration between the University and the East Kent Hospitals Neuro-Rehabilitation Unit, with the long term goal of testing the system in a clinical environment and up-scaling the system to larger robotic effectors.

Motor Imagery Based Continuous Teleoperation Robot Control with Tactile Feedback

“…The feasibility of inducing neurological recovery in paraplegic patients by long term training with a BCI-based gait protocol was shown in [5]. In addition, BCI-based control of virtual object [6], robotic arm [7][8][9], robotic prosthetic [10,11], wheelchair [12], and various rehabilitation devices [13][14][15][16] were also reported in previous research.…”

“…The subject was looking at the virtual cursor during the motor imagery, and could not see the robotic arm. By the motor imagery, the subjects learned to modulate their sensorimotor rhythm amplitude in the mu rhythm (8)(9)(10)(11)(12) Figure 2. Control architecture of BCI-driven continuous teleoperation robot system.…”

Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review

“…The evidence shows that the use of EEG signals within a BCI system is optimal and suitable when said system has the following elements: the incorporation of non-invasive and inexpensive equipment, good resolution, ease of use, and portability [ 77 , 78 , 79 ]. Also, the use of these signals for the development of BCI systems that support rehabilitation processes is natural and intuitive [ 80 ] since the process directly extracts information about the user’s motor intention. The use of EEG signals in these systems has received satisfactory results both in terms of system efficiency and patient satisfaction [ 44 ].…”

Assessment Methods of Usability and Cognitive Workload of Rehabilitative Exoskeletons: A Systematic Review

“…System Usability Scale (SUS). Reported by seven of the selected studies [24][25][26][27][28][29][30], this popular and well-accredited instrument was deployed to conduct quick evaluations of usability with users of a broad range of technological devices and interactive systems. It consists of a 10-item unidimensional psychometric questionnaire that is answered on a 5-point Likert-type scale.…”