Micro-Electronics for autonomous neural implants

Lead Research Organisation: Imperial College London
Department Name: Electrical and Electronic Engineering


Implantable neural interfaces can be used to connect human brains to artificial electronic circuits allowing e.g. control of computers by thoughts or treatment of various injuries and illnesses. An example of such is the possibility of treating spinal cord injuries by bypassing the damaged neural connection and allowing control of artificial limbs. This is achieved by inserting electrodes into neural tissue connected to instrumentation circuits recording electronic potentials generated by active neurons and decoding their meaning. While past solutions typically relied on recording of extracellular action potentials (EAPs), also known as neural spikes, generated by single neurons, the aim of this project is to focus on acquisition and processing of local field potentials (LFPs). This is as EAP-recording implants are typically hindered by limited lifetime due to the host body's foreign body response leading to scar tissue growth acting as a spatial and frequential low-pass filter, hence limiting the fidelity of recorded high-frequency EAPs. The low-frequency nature of LFPs allows for a significant reduction of the effect scar-tissue growth has on the recording.

Some of the involved challenges include selection of electrode material. This has to ensure minimal added thermal noise within the frequency band of LFPs when in contact with cerebrospinal fluid while at the same time being chemically inert and medically harmless. Preliminary results have shown Niobium (Nb) as a promising material suitable for such a recording as it is known to be biologically inert and is commonly used e.g. in dental implants. Its polarizability leads to generally smaller noise power densities in LFP frequency bands than commonly used platinum and tungsten making it a suitable candidate material for neural recordings. This is to be investigated by direct noise measurements in electrolytes and subsequently verified by direct in-vivo measurements.

Another challenge lies in the development of acquisition electronics which is greatly constrained by limits imposed on their power consumption governed by safety limits of heat dissipation in neural tissue on the order of 80 mW/cm^2. One of the techniques allowing reduction of used energy is clock-less, also known as continuous-time (CT), acquisition of signals. This approach leads to activity-dependent circuits that only use energy when activity is detected at their input. One of the aims of this project is to investigate the suitability and possible advantages of such circuits for acquisition of LFPs. As properties of such acquisition and sampling processes remain to a large extent unknown, mathematical simulations are used for their investigation. This is complemented by design of integrated circuits and practical measurements verifying hypotheses arising from theoretical models. This has already led to formulation of a minimal requirement to be satisfied in order to prevent aliasing similar to the Nyquist theorem which applies to ordinary sampling. In addition a novel method allowing reduction of flicker noise in CT sampling has been proposed and theoretically validated. An integrated circuit verifying this theory is to be designed, manufactured and tested.

This work is part of the ENGINI project with an aim to design the next generation of neural implants that achieve superior chronicity by being completely wireless, minimal in size, targeting LFP recordings and allowing formation of distributed implant networks.

The research aligns with the following EPSRC Research Areas: Assistive technology, rehabilitation and musculoskeletal biomechanics; Microelectronic device technology

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509486/1 01/10/2016 30/09/2021
1859713 Studentship EP/N509486/1 01/10/2016 01/04/2020 Michal Maslik
Description A new method of flicker noise suppression to be used in clock-less continuous-time acquisition systems was developed and presented.
Exploitation Route These findings might be e.g. used for development of novel acquisition systems to for next generation neural interfaces that are more power efficient due to their activity-dependent nature.
Sectors Electronics

URL https://ieeexplore.ieee.org/document/8584788