Brain- Computer Interface Techniques and its Application for Stroke Rehabilitation
Cuntai GUAN
2014-07-02
Abstract:
Brain-computer interface (BCI) technology has been actively investigated to provide an alternative way for rehabilitation to
help stroke survivors to restore motor function via activity-dependent brain plasticity. The hypothesis of using BCI for stroke
rehabilitation is built upon the following basis: (1) motor imagery activates primary motor cortex, pre-motor cortex, and possibly
some other parts of the brain which are responsible for motor control, therefore rehabilitation using motor imagery can lead to
motor recovery; (2) BCI provides a contingent feedback for stroke patients which helps reinforce the neural pathway of motor
control; (3) real-time feedback to patients provides motivation due to visual/auditory/haptic feedback; (4) combination of BCI
with mechanical stimulation (e.g. robotics) provides motor/sensory feedback which is beneficial. We conducted three pilot clinical
studies involving 66 stroke patients, where we investigated how BCI was used in conjunction with other devices (robotics, haptic,
and TDCS) to help hemiplegic stroke patients in performing upper limb rehabilitation. In this talk, we will present the design and
outcome of the studies. We observed statistically significant clinical outcomes in all three clinical studies comparing the pre-
and post-rehabilitation clinical measurements. Functional imaging shows statistically significant enhancement in functional conn-
ectivity after training. Initial indication of structural change might imply possible plasticity effects. EEG coherence provides
possible prediction for clinical outcome. We will also discuss several solutions to tackle one of the challenges in motor imagery
based BCI, non-stationarity of EEG.
About the speaker:
Biodata: Prof. Guan Cuntai is currently the Principal Scientist and Head of Neural & Biomedical Technology Department at the
Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore. He is the A*STAR MedTech
Programme Leader of Neuro-Technology. He received his PhD degree in electrical and electronic engineering from Southeast
University in 1993. Since 2003, he founded and directed the Brain-computer Interface (BCI) Laboratory, two medical technology
research programs and a research department at the Institute for Infocomm Research. His research interests include neural and
biomedical signal processing, machine learning and pattern recognition, neural and cognitive process, brain–computer interface,
neural image processing, and medical technologies. He is internationally recognized for his pioneering research work in BCI
based medical applications. He is the recipient of Annual BCI Research Award, IES Prestigious Engineering Achievement Award,
Achiever of the Year (Research) Award, etc. He published over 200 refereed papers and holds 14 granted patents and appli-
cations. He licensed 7 patents to industries. He is on editorial boards of IEEE Transactions on Biomedical Engineering (2010
-2013), IEEE Access (2012-2015), Journal of Brain Computer Interfaces, Australasian Medical Journal, Frontiers in Neuropr-
osthetics, etc.