ENGINI: Empowering Next Generation Implantable Neural Interfaces
Underpinning research related to ENGINI in research topics:
Current Project (2015-2020)
EPSRC Early Career Research Fellow: Timothy Constandinou
Research Team: Nur Ahmadi, Matthew Cavuto, Peilong Feng, Lieuwe Leene, Michal Maslik, Federico Mazza, Oscar Savolainen, Katarzyna Szostak
Collaborators: Andrew Jackson (Newcastle), Jinendra Ekanayake (Leeds)
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 several such devices implanted as freely-floating mm-scale probes for recording from the cortex. These 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.
- Ahmadi N; Constandinou T; Bouganis C, 2020: Improved Spike-based Brain-Machine Interface Using Bayesian Adaptive Kernel Smoother and Deep Learning. TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.12383600.v1
- Feng P, Maslik M and Constandinou TG, 2019, EM-Lens Enhanced Power Transfer and Multi-Node Data Transmission for Implantable Medical Devices, IEEE Biomedical Circuits and Systems Conference (BioCAS), Nara, Japan, 2019, pp. 1-4.
- Ahmadi N, Cavuto M, Feng P, Leene L, Maslik M, Mazza F, Savolainen O, Szostak K, Bouganis C, Ekanayake J, Jackson A, Constandinou T, 2019, Towards a Distributed, Chronically-Implantable Neural Interface, IEEE/EMBS Conference on Neural Engineering (NER), Pages: 1-6
- Ahmadi N, Constandinou T, Bouganis C, 2019, Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network, 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER 2019), Pages: 1-5
- Ahmadi N, Constandinou TG, Bouganis C-S, 2018, Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS), PLOS ONE, Vol: 13, ISSN: 1932-6203
- Feng P, Yeon P, Cheng Y, Ghovanloo M, Constandinou TG, 2018, Chip-Scale Coils for Millimeter-Sized Bio-Implants, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 12, Pages: 1088-1099, ISSN: 1932-4545
- Ahmadi N, Constandinou TG, Bouganis C-S, 2018, Spike Rate Estimation Using Bayesian Adaptive Kernel Smoother (BAKS) and Its Application to Brain Machine Interfaces., Pages: 2547-2550, ISSN: 1557-170X
- Szostak KM, Constandinou TG, 2018, Hermetic packaging for implantable microsystems: effectiveness of sequentially electroplated AuSn alloy, 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE
- Feng P, Constandinou TG, 2018, Robust Wireless Power Transfer to Multiple mm-Scale Freely-Positioned Neural Implants, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 363-366, ISSN: 2163-4025
- Leene LB, Maslik M, Feng P, Szostak KM, Mazza F, Constandinou TGclose, 2018, Autonomous SoC for Neural Local Field Potential Recording in mm-Scale Wireless Implants, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
- Mazza F, Liu Y, Donaldson N, Constandinou TG, 2018, Integrated Devices for Micro-Package Integrity Monitoring in mm-Scale Neural Implants, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 295-298, ISSN: 2163-4025
- Szostak KM, Grand L, Constandinou TG, 2017, Neural Interfaces for Intracortical Recording: Requirements, Fabrication Methods, and Characteristics, FRONTIERS IN NEUROSCIENCE, Vol: 11, ISSN: 1662-453X
- 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
- Szostak K, Mazza F, Maslik M, Feng P, Leene L, Constandinou TGclose, 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