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 NEW PLACE
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Delivering Remote Rehabilitation at Home: An Integrated Physio-Neuro Approach to Effective and User Friendly Wearable Devices
Banerji, S., Heng, J., Banerjee, A., Ponvignesh, P., Menezes, D., Kumar, R. (2017)
Converging Clinical and Engineering Research on Neurorehabilitation II, Springer (pp. 1097-1101)
Effect on activity and participation in a child with cerebral palsy using EEG Neurofeedback and the SynPhNe protocol in succession..
Banerji, S., Sadana, D., Jamuna, R., Banerjee, A., Kumar, R. (2016).
9th World Congress for Neurorehabilitation, USA
An Automated Physio-neuro Recovery Tool for Enhancing Muscle and Brain Co-ordination and Recovery After Sports Related Trauma and Injuries.
Banerji, S., Heng, J., Menezes, D., & Ponvignesh, P. (2014)
ICSST 2014 - Advanced Materials for Sports Technology, Singapore, December 2014
Augmenting Rehabilitation after Stroke: A Flexible Platform for Combining Multi-channel Biofeedback with FES.
Banerji, S., Heng, J., Ponvignesh, P., Menezes, D. (2013)
Springer, Berlin, Heidelberg (pp. 259–263)
A Physio-Neuro Approach to Accelerate Functional Recovery of Impaired Hand after Stroke.
Banerji, S., Kuah, C. W. K., Heng, J., & Kong, K. H. (2012)
Procedia Engineering, Vol 41, 257–263