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

Abstract

From the experimental results, it was found that EMG is a very reliable method for assistive technology control, provided that the user specific EMG calibration is done. With the EEG, even though the results were promising, due to insufficient detection of the signal, the controller was not adequate to be used within a neurorehabilitation environment. The survey indicated that the usability of the system is not a barrier for moving the system into clinical trials.Implication on rehabilitationFor the rehabilitation of patients suffering from neurological disabilities (particularly those suffering from varying degrees of paralysis), it is necessary to develop technology that bypasses the limitations of their condition. For example, if a patient is unable to walk due to the unresponsiveness in their motor neurons, technology can be developed that used an alternate input to move an exoskeleton, which enables the patient to walk again with the assistance of the exoskeleton.This research focuses on neuro-rehabilitation within the framework of the NHS at the Kent and Canterbury Hospital in UK. The hospital currently does not have any system in place for self-driven rehabilitation and instead relies on traditional rehabilitation methods through assistance from physicians and exercise regimens to maintain muscle movement.This paper summarises the development of a portable and cost-efficient BCI controlled assistive technology using a non-invasive BCI headset "OpenBCI" and an open source robotic arm, U-Arm, to accomplish tasks related to rehabilitation, such as access to resources, adaptability or home use. The resulting system used a combination of EEG and EMG sensor readings to control the arm, which could perform a number of different tasks such as picking/placing objects or assist users in eating.