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.
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A step towards home-based robotic rehabilitation: An interface circuit for EEG/SEMG actuated orthosis.
Heng, J., Kanna, S. (2009)
2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009, Singapore, July 2009 (pp. 1689–1694)
Quantitative EEG parameters for monitoring and biofeedback during rehabilitation after stroke.
Banerji, S., Heng, J., Raichur, A., Wihardjo, G. (2009)
2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009, Singapore, July 2009 (pp. 1998–2003)
New directions in the creation of upper extremity (UE) robotic rehabilitation devices for stroke patients.
Banerji, S., Heng, J., & Kangdra, W. (2008)
i-CREATe 2008 - International Convention on Rehabilitation Engineering and Assistive Technology 2008. 97-102.
EEG (mind controlled) system with four trigger states in a multi-level haptic devices for disabled persons.
Banerji, S., Heng, J., Kangdra, W. (2008)
i-CREATe 2008 - International Convention on Rehabilitation Engineering and Assistive Technology 2008. 92-96.