The world's first Brain Training Device has given a ray of new hope
to the recovery of survivors after stroke. Developed by researchers of
The Hong Kong Polytechnic University (PolyU)'s Interdisciplinary
Division of Biomedical Engineering (BME), this novel device which can
detect brainwave, and thereby control the movement of paralyzed limbs,
or go even further to control a robotic hand based on its sophisticated
The research was led by Prof. Raymond Tong Kai-yu, Professor of
PolyU's Interdisciplinary Division of Biomedical Engineering, who is
also the Principal Investigator of the award-winning Exoskeleton Hand
Robotic Training Device or the "Hand of Hope". His team members include
the BME research team (Newmen Ho, Xiaoling Hu, Ching-hang Fong, Xinxin
Lou, Lawrence Chong and Nathan Lam) and the Industrial Centre team of
PolyU (Robert Tam, Bun Yu, Shu-to Ng and Peter Pang).
The latest breakthrough "Brain Training Device" can be coupled with
the use of the "Hand of Hope" to achieve higher degree of recovery for
stroke patients. While effective motor recovery after stroke depends on
early rehabilitation program and intensive voluntary practice of the
paretic limbs, current rehabilitation products have not use brainwave to
guide the stroke survivors to identify voluntary intention and to
relearn how to reconnect to their paralyzed limb again.
Prof. Raymond Tong and his team therefore developed the Brain
Training Device with 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 the 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 iPad app." Prof. Raymond Tong explained.
The patented Brain Training System, which looks like a helmet for
cyclist and can read brainwaves, also has new features to find the
specific EEG electrode locations for each individual stroke patient and
reduce the number of EEG electrodes, which can reduce the system cost
and the preparation time for brain training, added by Prof. Tong.
To find a minimal set of electrodes to control the device with
accuracy higher than 90%, five chronic stroke patients were recruited to
be trained for 20 sessions in the study. The researchers found that, in
general, 32 electrodes are needed to maintain accuracy higher than 90%.
The high accuracy and low number of channels needed means that the Brain Training Device is a viable tool for assistive aid and rehabilitation training. The futuristic system will be made portable and easy-to-use at hospital and home settings.
PolyU researchers have already filed patents for this Brain Training
Device in both the United States and China. This project is funded by
the HKSAR Government's Innovation and Technology Fund (ITF). The
findings of this brain control algorithm have been published as the
cover story in top international journal IEEE Transactions on Neural
Systems and Rehabilitation Engineering (2011.12).