ENGINI: Empowering Next Generation Implantable Neural Interfaces
EPSRC Early Career Research Fellow: Dr Timothy Constandinou
Funding: Engineering and Physical Sciences Research Council (EPSRC) EP/M020975/1
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.
- Szostak K, Mazza F, Maslik M, Feng P, Leene L, Constandinou TG, 2017, Microwire-CMOS Integration of mm-Scale Neural Probes for Chronic Local Field Potential Recording, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 492-495
- Feng P, Constandinou TG, Yeon P, Ghovanloo M, 2017, Millimeter-Scale Integrated and Wirewound Coils for Powering Implantable Neural Microsystems, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Pages: 488-491
- Leene LB, Constandinou TG, 2017, A 0.016 mm2 12b ΔΣSAR with 14 fJ/conv. for Ultra Low Power Biosensor Arrays, IEEE Transactions on Circuits and Systems I: Regular Papers, Vol: 64, Pages: 2655-2665, ISSN: 1549-8328
- Leene LB, Constandinou TG, 2017, Time Domain Processing Techniques Using Ring Oscillator-Based Filter Structures, IEEE Transactions on Circuits and Systems I: Regular Papers, Pages: 1-10, ISSN: 1549-8328
- Leene LB, Constandinou TG, 2016, A 2.7μW/Mips, 0.88GOPS/mm2 Distributed Processor for Implantable Brain Machine Interfaces, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Pages: 360-363.
- Constandinou TG, Jackson A, 2016, Implantable Neural Interface, International Patent Application (PCT/GB2017/051417).