NON-INVASIVE SINGLE NEURON ELECTRICAL MONITORING (NISNEM Technology)

Lead Research Organisation: Imperial College London
Department Name: Dept of Bioengineering

Abstract

We propose the development of a new technology for Non-Invasive Single Neuron Electrical Monitoring (NISNEM). Current non-invasive neuroimaging techniques including electroencephalography (EEG), magnetoencephalography (MEG) or functional magnetic resonance imaging (fMRI) provide indirect measures of the activity of large populations of neurons in the brain. However, it is becoming apparent that information at the single neuron level may be critical for understanding, diagnosing, and treating increasingly prevalent neurological conditions, such as stroke and dementia. Current methods to record single neuron activity are invasive - they require surgical implants. Implanted electrodes risk damage to the neural tissue and/or foreign body reaction that limit long-term stability. Understandably, this approach is not chosen by many patients; in fact, implanted electrode technologies are limited to animal preparations or tests on a handful of patients worldwide. Measuring single neuron activity non-invasively will transform how neurological conditions are diagnosed, monitored, and treated as well as pave the way for the broad adoption of neurotechnologies in healthcare.
We propose the development of NISNEM by pushing frontier engineering research in electrode technology, ultra-low-noise electronics, and advanced signal processing, iteratively validated during extensive tests in pre-clinical trials. We will design and manufacture arrays of dry electrodes to be mounted on the skin with an ultra-high density of recording points. By aggressive miniaturization, we will develop microelectronics chips to record from thousands of channels with beyond state-of-art noise performance. We will devise breakthrough developments in unsupervised blind source identification of the activity of tens to hundreds of neurons from tens of thousands of recordings. This research will be supported by iterative pre-clinical studies in humans and animals, which will be essential for defining requirements and refining designs.
We intend to demonstrate the feasibility of the NISNEM technology and its potential to become a routine clinical tool that transforms all aspects of healthcare. In particular, we expect it to drastically improve how neurological diseases are managed. Given that they are a massive burden and limit the quality of life of millions of patients and their families, the impact of NISNEM could be almost unprecedented. We envision the NISNEM technology to be adopted on a routine clinical basis for: 1) diagnostics (epilepsy, tremor, dementia); 2) monitoring (stroke, spinal cord injury, ageing); 3) intervention (closed-loop modulation of brain activity); 4) advancing our understanding of the nervous system (identifying pathological changes); and 5) development of neural interfaces for communication (Brain-Computer Interfaces for locked-in patients), control of (neuro)prosthetics, or replacement of a "missing sense" (e.g., auditory prosthetics). Moreover, by accurately detecting the patient's intent, this technology could be used to drive neural plasticity -the brain's ability to reorganize itself-, potentially enabling cures for currently incurable disorders such as stroke, spinal cord injury, or Parkinson's disease. NISNEM also provides the opportunity to extend treatment from the hospital to the home. For example, rehabilitation after a stroke occurs mainly in hospitals and for a limited period of time; home rehabilitation is absent. NISNEM could provide continuous rehabilitation at home through the use of therapeutic technologies.
The neural engineering, neuroscience and clinical neurology communities will all greatly benefit from this radically new perspective and complementary knowledge base. NISNEM will foster a revolution in neurosciences and neurotechnology, strongly impacting these large academic communities and the clinical sector. Even more importantly, if successful, it will improve the life of millions of patients and their relatives

Planned Impact

Our vision is to develop clinically applicable, non-invasive single neuron monitoring (NISNEM) technology to advance the frontiers of neural interfacing. NISNEM has the potential to transform healthcare and directly impact the healthcare sector, the research and innovation sector, the medical technology industry as well as the national economy and social wellbeing. We aim to create a broad scientific framework for a revolutionary technology that would potentially impact virtually every field of the medical industry. The advances in understanding, monitoring and treating neurodegenerative diseases that NINSEM would enable will set the basis for a new form of technology-based healthcare. This technology, together with ongoing efforts on artificial intelligence (AI), would position the UK as a world-leader in next generation healthcare.
NISNEM would allow studies that increase our understanding of neurological diseases. Many neurological diseases (e.g., Parkinson's, Alzheimer's, Huntington's) are unique to humans; animal models are extremely useful, but, in reality, limited due to the lack in complexity. Having the means to record from large neural populations in patients would constitute a cornerstone to advance our knowledge on these diseases. This improved understanding would enable new targeted pharmacological and neurotechnology-based treatments that are more effective than current methods at treating neural conditions and have minimal side effects. Neurological diseases and conditions are a vast socioeconomic drain on society. For example, currently in UK there are 1.2 million stroke survivors and >100,000 new stroke cases every year; this incidence rate is forecasted to increase by approximately 60% over the next 20 years (stroke.org.uk, 2019) because of population ageing.
As NISNEM is a non-invasive technology, it holds the potential to extend healthcare from the hospital to the home, enabling continued and personalised healthcare. These achievements will have a tremendous social and economic impact, and imply a radical cultural change in how healthcare is provided.
The proposed research will have dramatic impact on the academia and industry. In particular, it will bring a completely new perspective, new technologies/tools and a complementary knowledge base to the biomedical/neural engineering community. The program will also be extremely relevant for the neuroscience community at large, since the proposed methods will enable a breadth of novel approaches to study the nervous system, especially in humans. The technological advances required to produce NISNEM in conjunction with the diverse applicability of the proposed technology are strong indicators that this project could foster a revolution in neurosciences and neurotechnology, strongly impacting and bridging these large academic communities. The research would also be extremely relevant for academics in the broader healthcare engineering and clinical neurology fields since we will develop and progress new techniques and applications of neural interfacing for clinical use, ultimately conditioning all aspects of healthcare.
The potential impact of the proposed research programme has been recognised by a number of leading companies, which have provided their strong support (~£2M in-kind contribution), and expressed a strong interest in discussing the co-creation phase following the project (see LoS from industries). Moreover, key patients' associations have enthusiastically supported the vision of the project and its focus on clinical viability (see LoS).
 
Title A platform to study and restore high-dimensional behaviour in rodents 
Description This novel experimental paradigm enables the investigation of brain-wide dynamics, muscle activity, and movement kinematics during high dimensional behaviour in head-fixed mice. This setup consists of: i) a 3D treadmill enabling unrestricted 2D running behaviour in head-fixed mice; ii) an odor delivery system for cued running; iii) an "earthquake" machine for investigation of automated corrective behaviour in response to rapid positional perturbations on the treadmill; iv) a multi-camera setup enabling extracting of movement kinematics via pose estimation algorithms; v) a multi-probe micromanipulator system for precise positioning of up to 4 Neuropixels probes; vi) a 32 channel EMG acquisition system; and vii) a closed-loop system for auditory-based BCI tasks. 
Type Of Material Improvements to research infrastructure 
Year Produced 2022 
Provided To Others? No  
Impact This experimental setup will enable the investigation of brain-wide contributions to motor control during both cued and spontaneous behaviour, and in response to mechanical perturbations. It will also serve as a platform to develop BCI tasks. Additionally, it will be used to validate the novel electrodes that are being developed in NISNEM, via decoding of movement kinematics and muscle activity from brain recordings during running behaviour, and in performance of the BCI task. 
 
Title A stretchable HD-sEMG sleeve for control of wearable devices such as prosthetics and exoskeletons 
Description We have developed a fabrication strategy that can convert a regular sEMG grid (available commercially) in which the individual electrodes are connected via a flexible PCB into a single stretchable HD-sEMG sleeve. The resultant fabrication allows for the obtained grid to be applied as a patch (e.g. across the chest muscles) or as a sleeve (e.g. around the forearm or shin) and the stretchable nature of the sleeve allows for the entire grid to compression fit across the body, allowing for better quality of signals and for the same grid to be easily applied across individuals of varying anatomy without the need for customised solutions. This development is part of the translational area of NISNEM that aim at translating the NISNEM interfacing technologies into assistive and rehabilitation devices. 
Type Of Material Improvements to research infrastructure 
Year Produced 2022 
Provided To Others? No  
Impact While the system is under development, once completed, the developed stretchable and wearable grid will be integrated into a textile base resulting in a textile-based structure that can be worn along with a soft/rigid exoskeleton or as a layer in a prosthetic fitting. Outside applications for assistive devices and prosthetics, it could also form part of commercial solutions in research applications as well requiring for conformable HD-EMG systems. The same fabrication strategy can be even scaled outside HD-EMG towards other high-density sensing systems, such as accelerometers to sense tactile feedback, propagation of sensory information across the skin etc. 
 
Title An actuation pack for simultaneous assistance of coupled multi-DoF joints 
Description Exosuits (soft exoskeletons) primarily use a soft embodiment coupled with actuation systems like motors, pneumatic, hydraulic or other systems to provide assistance or resistance to the wearer with an aim to achieve therapeutic or assistive outcomes. Systems driven by motors through cable-based power transmission are widely accepted as a design archetype capable of providing the highest power-to-weight ratio, enabling the system to become wearable and portable. However, one downside of current systems is that one motor is used for either providing the agonist and antagonist movement for a single-DoF joint or actuating a single cable for a multi-DoF joint. This is because a single set of pulleys cannot span the entire range of values required for assisting a movement of coupled joints such as the shoulder, wrist or ankle. Therefore, to assist a complex system such as our upper-limbs, the current state-of-the-art systems would end up requiring a large number of actuators and sensors making the system bulky and impractical. We are in the process of developing a new actuation pack system which requires the theoretically minimum number of actuators and sensors (i.e. one each) for a single DoF irrespective of whether the joint is part of a coupled multi-DoF joint such as the wrist or ankle or a simpler 1-DoF joint such as the knee or elbow. This development is part of the translational area of NISNEM that aim at translating the NISNEM interfacing technologies into assistive and rehabilitation devices. 
Type Of Material Improvements to research infrastructure 
Year Produced 2022 
Provided To Others? No  
Impact While the system is currently still under development, once tested and finalised, we should be able to considerably reduce the size, complexity and cost impact by close to 50%. This would allow for many of the currently available assistive devices such as exoskeletons to feature this design approach and reduce the mechatronic requirements considerably. 
 
Title High-density (hd) stainless-steel arrays for surface EMG detection (100-400 electrodes) 
Description One of the major goals of the project is to fabricate a polymer-based high-density (HD) non-invasive interface which is aimed to be flexible and bio-compatible. While the fabrication of this novel HD-EMG matrix is underway, comprehensive trial and testing are required for high-density systems in order to design the processing and information extraction algorithms. Therefore, to facilitate development, testing, refining and validation of our algorithms and models, a HD-EMG grid of 10x10 stainless steel electrodes with an inter-electrode distance of 2mm and an electrode diameter of ~500µm has been fabricated. The developed HD-sEMG stainless steel arrays will be used to record and decompose sEMG signals and will also be used to benchmark the polymer-based electrode technology. Concurrently, the obtained HD-sEMG data will be used to train and inform our models and decomposition algorithms. Currently, this 100-channel grid is being used to record sEMGs from the abductor pollicis brevis of the palm and flexor and extensor muscles of the forearm. As a next step, we are currently working on a 20x20 matrix of 400-channels with an inter-electrode distance of 1.25 mm. This new matrix which is currently under development is aimed at understanding the theoretical and practical limit of the electrode density which will further allow us to inform and optimise the design of the polymer-based HD-sEMG matrix to enable us to optimize the detection of MUs. 
Type Of Material Improvements to research infrastructure 
Year Produced 2022 
Provided To Others? No  
Impact Experiments have been scheduled to extract information about the minimum required inter-electrode distance and an estimation of maximum identifiable MUs from a given muscle over. Simultaneously, the data recorded from these electrodes will be also used to improve EMG generation models and decomposition algorithms. 
 
Title Methods for low-noise, high-gain and high-precision biosignal recording in the presence of strong electrochemical DC offsets & stimulation artefacts. 
Description The novel method allows for the high-quality recording of EMG (and other bioelectric, e.g. EEG, ECG) signals even in the presence of very strong offsets; such offsets are often introduced by dry electrodes fabricated by means of experimental technologies and processes. The method: i) can be adopted either in low area profile PCB designs and/or monolithic (microchip) designs, ii) has led to the realisation of novel, "offset-robust" front-ends for the acquisition of EMG (but also of ECG and EEG) signals and iii) has been successfully tested with experimental/prototyped PEDOT PSS electrode arrays. 
Type Of Material Improvements to research infrastructure 
Year Produced 2021 
Provided To Others? No  
Impact Realisation of compact (area of ~ 2 business cards), high-count (256 channels), wearable/portable, battery-operated EMG recording devices/modules. 
 
Title Myolink-2: Miniaturised offset-robust 256-channel wearable EMG recording device 
Description Wearable battery-operated 256-channel micro-instrument benefiting from novel offset-robust recording channel topologies and state-of-art performance. Enables high-density sEMG recordings in a mobile setting. SUMMARY Number of channels: 256 Volume: 10.2cm x 6.7cm x (<1cm) Midband gain: 46dB (or 200V/V) up to 67 dB (or 2400 V/V) Bandwidth: 10Hz-500Hz Power supply: 3V (single) DC offset rejection: rail-to-rail Total power consumption: 32 mA drawn from (single) +3V power supply: 96 mWs Integrated noise: 600nV rms (10-500Hz) Input impedance: 5G? 
Type Of Material Improvements to research infrastructure 
Year Produced 2022 
Provided To Others? No  
Impact The methods and architectures adopted for the realisation of Myolink-2 enable the de-risked migration to a third generation of low area profile and lower power EMG micro-instruments comprising 1024 high-performance recording channels. 
 
Title An AI-based EMG Generation Model - MorphWave 
Description MorphWave is an AI-based EMG generation model, that takes a novel approach to traditional analytical and numerical methods. The developed method (MorphWave) simulates motor unit action potentials (MUAPs) under a great number of conditions in a short time. MorphWave achieves this performance using conditional generative adversarial networks (GANs) to transform the input MUAPs to synthetic new ones with modified attributes. The model is broadly made up of two components - the generator and the discriminator. The generator component of the model is trained with source MUAPs (obtained from experimental data) and conditions to change (such as position of the motor unit, number of fibres etc.) to generate simulated MUAPs, representative of the changed conditions. Another module named discriminator is trained to distinguish between the artificially generated MUAPs from the real ones. Together, the generator and the discriminator compete with each other such that the generator trains itself to output more and more realistic MUAPs that are indistinguishable from the real experimentally-obtained source MUAPs. In MorphWave, the model has been trained while changing a total of six conditions including: 1). the depth of the motor unit, 2). the medial-lateral position of the motor unit, 3). the number of fibres, 4). the location of the innervation zone, 5). the propagation velocity of the current source, and 6) the fibre length. This development is part of the signal processing and AI activities of NISNEM. These modelling developments allow us, on the one hand, to simulate realistic signals to test/validate the processing algorithms and, on the other hand, to be the basis of new decoding algorithms that use the forward modelling information to solve the inverse problem. 
Type Of Material Computer model/algorithm 
Year Produced 2022 
Provided To Others? No  
Impact To the best of our knowledge, this is the first AI-based EMG generation model. Even with only one MUAP, MorphWave is capable of generating a large number of variations of this MUAP in a short time by feeding the model with diverse conditions. We see our developed model to be used for data augmentation, inverse modelling, and to test the performance of new EMG decomposition algorithms. 
 
Title Real-time decomposition of multi-channel EMG signals recorded by thin-film electrode arrays in humans 
Description Electromyography (iEMG) decomposition is a mathematical technique that aims to identify motor neuron (MN) discharge timings from interference EMG recordings. When performed in real-time, the extracted neural information can be used for establishing high information transfer human-machine interfaces capable of achieving natural and intuitive control of devices, such as prosthetics and exoskeletons. We developed a real-time decomposition algorithm based on a Hidden Markov Model of EMG. This pattern analysis and classification method uses GPU-implemented Bayesian filter to estimate the spike trains of motor units (MU) and their action potentials (MUAPs). Moreover, parallel implementation in multiple GPU clusters was enable to maintain real-time performance in a multi-channel case. The Bayesian modelling and filtering was implemented through a multi-channel framework that processes neighbouring channels which share the activity of the same MUs. Additionally, a decomposed-checked channel strategy handles MU activities appearing in distant channels, and assigns channels into different groups to be processed in related GPU clusters. The algorithm was validated on six 16-channel simulated signals generated by an accurate model of EMG generation, and three 32-channel experimental signals acquired from the tibialis anterior muscle with thin-film array electrodes. All signals were decomposed in real time with an average decomposition accuracy >90%. This development is part of the signal processing and AI activities of NISNEM that aim at accurate decoding of multichannel signals into individual neural sources (in this case, individual spinal motor neuron activities). 
Type Of Material Computer model/algorithm 
Year Produced 2022 
Provided To Others? No  
Impact Our proposed multi-channel EMG decomposition algorithm can process multi-channel signals and is capable of tracking 20-30 motor units (MUs) simultaneously in real-time with high accuracy. This performance enables our method to be applied to multi-channel electrode arrays to establish human-machine interfaces with high-information transfer, allowing for accurate and real-time control of robotic systems such as exoskeletons and prosthetics. The proposed multi-channel EMG decomposition algorithm establishes a firm foundation for further research in novel spike sorting algorithms. Based on the proposed Bayesian modelling and filtering, the team will aim to develop new AI-based methods capable of efficiently discarding unnecessary scenarios, reducing computational time, and increasing convergence speed. 
 
Title Ultra-fast and highly realistic numerical modelling of surface EMG 
Description A fundamental component of the development of methods for information extraction from biosignals is the availability of forward generation models. Models provide the means to benchmark processing algorithms, to augment data for training deep learning networks, and to solve the inverse problem. However, current biosignal models are either simplifications of the experimental conditions or require impractical computational resources and time. We have developed a computationally efficient and realistic model of electric fields in body tissues (volume conductor), with a specific application to electromyography (EMG). The volume conductor is described from magnetic resonance images (MRI) of tissue surfaces by numerical discretization. The numerical solution of the forward model is optimized by reducing the main calculations to the solutions in a minimal number of basis points, from which the general (scalable) solution is then derived. This new numerical method allows for almost real-time simulations, without any constraints on the complexity of the volume conductor or of the electric sources. These numerical modelling developments have been fundamental for advancing AI-based modelling (this numerical model served as training for the AI-based models), here described as another output of the project. 
Type Of Material Computer model/algorithm 
Year Produced 2021 
Provided To Others? Yes  
Impact The computational time magnitude of previous state-of-the-art models is, in the best cases, in the order of hours for a single simulation (with a fixed set of model parameter values, approximately 50000 muscle fibres, and 5 recording electrodes. By exploiting the mathematical properties of the forward equations and source model, we were able to achieve a computational performance of the order of minutes per simulation. Moreover, in our model, changing most of the simulation parameters does not require recomputing the whole model and reduces computational time of new simulations to the order of seconds, if the volume conductor remains constant. As a result, it becomes practically possible to simulate arbitrary large datasets of highly realistic EMG signals with high variability in the simulation parameters. This is fundamental for data augmentation or even for building digital twins and directly train decoding algorithms on simulated data. 
URL https://www.biorxiv.org/content/10.1101/2021.06.07.447390v1.full.pdf
 
Description EPSRC Network+: Neurotechnology for enabling community-based diagnosis and care 
Organisation University of Birmingham
Country United Kingdom 
Sector Academic/University 
PI Contribution Because of the visibility of the work performed in NISNEM, my team has been invited to participate to a EPSRC Network+ application on neurotechnology for diagnosis and care, led by the University of Birmingham. We have just been informed that this network has been funded. Diagnostics is one of the future core applications of NISNEM technology and therefore this network will be fundamental for disseminating and further expanding the potential applications of the output of the project. My team fully participated to the conceptualization of the network focus, contributing specifically with the inclusion of non-invasive technologies for diagnostics in the network. Moreover, the focus of our network is the development of technologies that shift the emphasis of diagnosis and management of neurological conditions from hospital to non-hospital environments, which is also a core target of NISNEM.
Collaborator Contribution The partners in NISNEM contributed to the concepts included in the network proposal. The network is fully within the scope of NISNEM and will serve to broaden the dissemination of NISNEM technologies.
Impact The EPSRC Network+ on Neurotechnology for enabling community-based diagnosis and care will commence its activities this year. Updates will be reported in the coming reports.
Start Year 2021
 
Description EPSRC-Facebook/Meta co-sponsored PhD scholarship on human augmentation by wearable systems 
Organisation Facebook
Country United States 
Sector Private 
PI Contribution This is a joint sponsorship of PhD scholarship by the EPSRC CDT AI4Health (Imperial College) and Facebook Reality Labs for a 4-year project dedicated to human augmentation. Facebook is a partner of NISNEM and agreed to support the project in both direct financial contribution and in-kind. For this scholarship, in addition to the co-sponsoring of the scholarship, Facebook also provided wearable prototype technology from their labs (division of Ctrl-Labs) as well as time of their R&D team for regular meetings and discussion. The contribution of my team to this collaboration is in the provision of NISNEM methods for extracting information at the spinal motor neuron level from skin sensors (algorithms, AI) and the development of approaches that would allow augmenting human motor functions (neuroscience research).
Collaborator Contribution Etienne Burdet, co-I in NISNEM, is providing his expertise in human augmentation and robotics for this collaboration. Juan Gallego, co-I in NISNEM, is also collaborating on this topic. Juan Gallego and my team have secured a PhD student to extend on these topics (Imperial President's scholarship confirmed and offer accepted by the student to start in the academic year 2022-2023).
Impact A first scientific paper resulting from the first year of this collaboration is ready for submission. The PhD student has successfully passed the Early Stage Assessment and preparing for her Late Stage Assessment.
Start Year 2021
 
Description EPSRC-Huawei co-sponsored PhD scholarship on minimally invasive wearable systems 
Organisation Huawei Technologies Research and Development UK Ltd
Country United Kingdom 
Sector Private 
PI Contribution This is a co-sponsored PhD scholarship by Huawei within the CDT in AI4Health (Imperial). The project is on the development of thin-film subcutaneous (epimysial) electrode arrays and decoding algorithms for next generation minimally invasive interfaces with VR and AR. The project is well in line with the developments of NISNEM (Huawei is a partner of NISENM). My team is providing the technology (electrodes and hardware), as well as the algorithms for signal decoding.
Collaborator Contribution Juan Gallego, co-I of NISNEM, will contribute to the animal tests of the proposed electrodes. The team of Rylie Green is testing PEDOT coating of the thin-film electrodes.
Impact The PhD project has started in 2021. The student has successful passed the early stage assessment and is preparing for the late stage assessment. The first human experimental implants will be performed in spring 2022 (ethical approval already secured).
Start Year 2021
 
Description NeuroMod+: Co-creation for next-generation neuromodulation therapeutics 
Organisation University of Oxford
Country United Kingdom 
Sector Academic/University 
PI Contribution My research team has contributed to the concept of the network, with discussions on the content, mainly in relation to NISNEM technologies.
Collaborator Contribution Rylie Green, co-I in NISNEM, is co-I in this network proposal and extensively contributed to the conceptualization of the network focus. The network will focus on addressing the challenge of minimally invasive treatments for brain disorders, including non-invasive methods developed in NISNEM for monitoring and treating. The network has just been accepted for funding and includes new collaborations with the University of Oxford, the University of Nottingham and the University of Edinburgh. This network and the other newly funded EPSRC Network+ on Neurotechnology for enabling community-based diagnosis and care (see separate item in this section of the report) cover two fundamental core areas of NISNEM - treatment and diagnosis of neurological disorders - and will be essential in accelerating the dissemination of NISNEM technologies.
Impact This network will commence this year. We will update on the outputs in the following reports.
Start Year 2021