The Hong Kong Polytechnic University, Hong Kong, China

Ateam of researchers in the Interdisciplinary Division of Biomedical Engineering (BME) at The Hong Kong Polytechnic University (PolyU) say that they have created the world’s first brain training device, which can detect brainwaves, and use them to control the movement of limbs paralyzed from stroke, or to even control a robotic hand based on its sophisticated algorithm.

Fig. 1 – Developed by researchers PolyU, the device can detect brainwaves to help control the movement of paralyzed limbs using a sophisticated algorithm. (Credit: The Hong Kong Polytechnic University)

The researchers, led by Professor Raymond Tong Kai-yu, who is also the Principal Investigator of an award-winning exoskeleton hand robotic training device, say that the latest device can be coupled with the use of robotic device to achieve an even higher degree of recovery for stroke patients.

While effective motor recovery after stroke depends on early rehabilitation and intensive voluntary exercise of the weakened limbs, current rehabilitation products have not used brainwave technology to guide stroke survivors to identify voluntary intention and to relearn how to reconnect to their paralyzed limb again.

The scientists developed the brain training device by incorporating a new coherence algorithm for hand function training. The new algorithm is based on frequency coherence on surface electroencephalography (EEG, brainwave) and electromyography (EMG, muscle activities) to identify voluntary intention and their connection.

“The Brain Training Device is able to guide stroke patients to relearn the reconnection between the brain and the limb, with a new design on the EEG headset and the EMG forearm brace to transmit data for controlling a hand robotic system interfaced by a telecare software platform using an iPad app,” Tong explained.

The patented Brain Training System, which looks like a cyclist helmet and can read brainwaves, also has features to find specific EEG electrode locations for each individual stroke patient and reduce the number of EEG electrodes, which can reduce the system’s cost and the preparation time for brain training, added Tong. (See Figure 1)

Five chronic stroke patients were recruited to be trained for 20 sessions in the study, in order to determine a minimum set of electrodes to control the device with an accuracy greater than 90 percent. The researchers found that, in general, 32 electrodes are needed to maintain that high accuracy. They say that the high accuracy and low number of channels needed makes the brain training device a viable tool for assistive aid and rehabilitation training. The futuristic-looking system is easily transportable for both hospital and home settings.

PolyU researchers have already filed patents for this Brain Training Device in both the US and China. This project is funded by the HKSAR Government’s Innovation and Technology Fund (ITF). Their research findings on the brain control algorithm have been published in the international journal, IEEE Transactions on Neural Systems and Rehab litation Engineering.