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Details of Grant 

EPSRC Reference: EP/H019472/1
Title: Advanced Algorithms for Neural Prosthetic Systems
Principal Investigator: Ghahramani, Professor Z
Other Investigators:
Rasmussen, Professor C
Researcher Co-Investigators:
Mr JP Cunningham
Project Partners:
Department: Engineering
Organisation: University of Cambridge
Scheme: Standard Research
Starts: 04 January 2010 Ends: 30 November 2013 Value (£): 407,739
EPSRC Research Topic Classifications:
Artificial Intelligence Human-Computer Interactions
Robotics & Autonomy Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
20 Nov 2009 ICT Prioritisation Panel (Nov 09) Announced
Summary on Grant Application Form
Our seemingly effortless ability to make coordinated movements belies the sophisticated computational machinery at work in our nervous system. In recent years, the field of neuroscience has been dramatically expanding the complexity of its data acquisition technologies and experiments. This technological development has created a preponderance of valuable experimental data, but the analytical methods required to deeply interrogate this data have not yet been developed. Simultaneously, the last decade has seen major advances in the fields of computational statistics, data analysis techniques, and machine learning. Research in these areas has enabled investigation into and understanding of previously uninterpretable data.This proposal seeks to bring together key research from these two fields to significantly advance the scientifically and medically important application of neural prosthetic systems, which seeks to improve greatly the quality of life of hundreds of thousands of severely disabled human patients worldwide. Debilitating diseases like Amyotrophic Lateral Sclerosis can leave a human without voluntary motor control. However, in most cases, the brain itself remains intact and has normal function. The same is true with spinal cord injuries that result in severe paralysis. In fact, tetrapalegic patients list ``regaining arm/hand control'' as the top priority for improving their quality of life, as regaining this function would allow significant patient independence. To address this priority, neural prosthetic systems seek to access the information in the brain and use that information to control a prosthetic device such as a robotic arm or a computer cursor. There are many medical, scientific, and engineering challenges in developing such a system, but all neural prosthetic systems share in common a decoding algorithm. Decoding algorithms map neural activity into physical commands such as parameters for controlling a robotic arm. Current decoding approaches have shown exciting proofs of concept, but there are a number of shortcomings that must be addressed before the field produces a clinically viable prosthetic device with speed and accuracy comparable to a healthy human arm. Our research programme will use advanced statistical and machine learning technologies to create algorithms that can decode neural activity with higher precision that previously seen. We have identified several opportunities for meaningful improvement, from incorporating the statistics of natural reaching to validating these algorithms in a realistic online setting. Taken together, these algorithmic developments should help create a much higher quality neural prosthetic device.
Key Findings
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Potential use in non-academic contexts
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Organisation Website: http://www.cam.ac.uk