OUR SCIENCE
We are committed to translating brain and muscle science into brain+muscle training that leads to independence
Our SynPhNe™ Learning Model
Our SynPhNe™ Learning Model is adapted from the scientific principles of developmental biology and modern neuroscience. It leverages the training principles of mainstream therapy, postural alignment and energy management, including some based on techniques of yoga and martial arts.
Our technology works with the most fundamental and measurable signals that our brain and muscles give out (EEG & SEMG signals). This allows us to see what underlying neurological and physiological issues driving our symptoms, behaviors and experience are in real-time, thus enabling us to effectively and efficiently tackle them head-on. Because SynPhNe can track “micro-changes” in brain and muscle activities, SynPhNe can help break out of “plateaus”.
Our SynPhNe™ wearable technology increases the chances of recovery by mimicking how a human baby learns in the first few months after birth, when neuroplasticity is at its most efficient.
Click here to learn more about our four SynPhNe™ flagship programs that are specially designed for Neurological Disabilities, Learning Difficulties, Effects of Ageing and Chronic Stress & Pain.
Our Publications
2019 - 2018 | 2017 - 2012 | 2012 - 2009 | 2009 - 2008
OUR NEW PLACE
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Facilitating early onset of therapy after stroke: An Arm Glove design for self-regulation of muscle activation.
Banerji, S., Heng, J., Pereira, B. (2012)
i-CREATe 2012 - 6th International Convention on Rehabilitation Engineering and Assistive Technology
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Unified EEG-SEMG Platform for Accelerated Recovery of Motor Function after Stroke.
Banerji, S., Heng, J., Pereira, B. (2012)
ISEK 2012 Annual Congress, Australia
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A step towards multi-level human interface devices: a system that responds to EEG/SEMG triggers.
Banerji, S., Heng, J. (2010)
International Journal of Biomechatronics and Biomedical Robotics, 1(2), 93.
A unified, neuro-physio platform to facilitate collaborative play in children with learning disabilities.
Banerji, S., Heng, J. (2009)
2009 IEEE International Conference on Rehabilitation Robotics, ICORR 2009, Kyoto, Japan, June 2009
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Low Usage of Intelligent Technologies by the Aged: New Initiatives to Bridge the Digital Divide.
Heng, J., Banerji, S. (2010)
Intelligent Technologies for Bridging the Grey Digital Divide, IGI Publishers, September 2010. (pp 188-206)