Movements of different parts of the body, including the arm, start when the brain produces specific electrical signals. Sometimes after a stroke, even though the person intends to move the arm, the arm will not move or will only move partly. The brain continues to produce electrical signals after stroke, but when the signals are weak the arm does not move. Brain-computer interface (BCI), is a method of utilising the brain signals to produce useful outputs via computers. One type of BCI is BCI Functional Electrical Stimulus (BCI-FES) which is a device that can detect these brain signals produced during the intention to move the weak arm after a stroke. It then uses these signals to trigger an electrical stimulator. The electrical stimulator then stimulates the muscles in the weak arm to produce the desired arm movement. Recent studies show that rehabilitation using this device has given better results when compared with conventional rehabilitation approaches. However, the BCI-FES devices currently available are bulky and participants need to come to the hospital or laboratory to receive the treatment. Our project aims to develop a BCI-FES device, called Tele BCI-FES that can be used at stroke participant's home. The investigators are using techniques like machine learning to make the Tele BCI-FES quick and simple to set up, easy to use and effective. This study aims to investigate if the proposed Tele BCI-FES device is a feasible and acceptable intervention for post-stroke rehabilitation.
The study aims to identify whether stroke survivors can intentionally modulate their brain signals for controlling an FES device to produce arm movements for home based rehabilitation. The study also assess the participants acceptability of this new home-based rehabilitation.