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Details of Grant 

EPSRC Reference: EP/M020975/1
Title: Empowering Next Generation Implantable Neural Interfaces
Principal Investigator: Constandinou, Dr TG
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Georgia Institute of Technology Michigan State University Newcastle University
UCL
Department: Electrical and Electronic Engineering
Organisation: Imperial College London
Scheme: EPSRC Fellowship
Starts: 01 August 2015 Ends: 31 July 2020 Value (£): 1,016,559
EPSRC Research Topic Classifications:
Electronic Devices & Subsys. Med.Instrument.Device& Equip.
RF & Microwave Technology
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
05 Mar 2015 EPSRC ICT Fellowships Interview Meeting 5 March 2015 Announced
27 Jan 2015 EPSRC ICT Prioritisation Panel - Jan 2015 Announced
Summary on Grant Application Form
Being able to control devices with our thoughts is a concept that has for long captured the imagination. Neural Interfaces or Brain Machine Interfaces (BMIs) are devices that aim to do precisely this. Next generation devices will be distributed like the brain itself. It is currently estimated that if we were able to record electrical activity simultaneously from between 1,000 and 10,000 neurons, this would enable useful prosthetic control (e.g. of a prosthetic arm). However, rather than relying on a single, highly complex implant and trying to cram more and more channels in this (the current paradigm), the idea here is to develop a simpler, smaller, well-engineered primitive and deploy multiple such devices. It is essential these are each compact, autonomous, calibration-free, and completely wireless. It is envisaged that each device will be mm-scale, and be capable of recording only a few channels (i.e. up to 20), but also perform real-time signal processing. This processing will achieve data reduction so as to wirelessly communicate only useful information, rather than raw data, which can most often be just noise and of no use. Making these underlying devices "simpler" will overcome many of the common challenges that are associated with scaling of neural interfaces, for example, wires breaking, biocompatibility of the packaging, thermal dissipation and yield. By distributing tens to hundreds of these in a "grid" of neural interfaces, many of the desirable features of distributed networks come into play; for example, redundancy and robustness to single component failure. A first tangible application for this platform will see these devices embedded in a uniform array within a flexible substrate for electrocorticography (i.e. recording from the surface of the brain). It will however, also be investigated how the underlying devices can be made applicable to other formats, for instance, in penetrating intracortical devices (recording from within the cortex). Such devices will communicate the neural "control signals" to an external prosthetic device. These can then, for example, be used for: an amputee to control a robotic prosthetic; a paraplegic to control a mobility aid; or an individual with locked in syndrome to communicate with the outside world.

This Fellowship will consolidate expertise and build a core capability that can deliver such devices. This will be achieved by working together with researchers and professionals across multiple disciplines including ICT, engineering, healthcare technologies, medical devices and neuroscience. The research is extremely well aligned with the current quest to understand the brain; for example, US presidential BRAIN initiative, and the EU human brain project. It will impact neuroscience research, by extending current capabilities by at least an order of magnitude, but also medical devices by inventing and demonstrating a radically new approach.

Key Findings
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Potential use in non-academic contexts
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Organisation Website: http://www.imperial.ac.uk