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Microwave imaging has been investigated for medical applications such as stroke and breast imaging. Current systems typically rely on bench-top equipment to scan at a variety of antenna positions. For dynamic imaging of moving structures, such as the cardiovascular system, much higher imaging speeds are required than what has thus far been reported. Recent innovations in radar-on-chip technology allow for simultaneous high speed data collection at multiple antenna positions at a fraction of the cost of conventional microwave equipment, in a small and potentially portable system. The objective of the current work is to provide proof of concept of dynamic microwave imaging in the body, using radar-on-chip technology.Arrays of body-coupled antennas were used with nine simultaneously operated coherent ultra-wideband radar chips. Data were collected from the chest and thigh of a volunteer, with the objective of imaging the femoral artery and beating heart. In addition, data were collected from a phantom to validate system performance. Video data were constructed using beamforming.The location of the femoral artery could successfully be resolved, and a distinct arterial pulse wave was discernable. Cardiac activity was imaged at locations corresponding to the heart, but image quality was insufficient to identify individual anatomical structures. Static and differential imaging of the femur bone proved unsuccessful.Using radar chip technology and an imaging approach, cardiovascular activity was detected in the body, demonstrating first steps towards biomedical dynamic microwave imaging. The current portable and modular system design was found unsuitable for static in-body imaging.This first proof of concept demonstrates that radar-on-chip could enable cardiovascular imaging in a low-cost, small and portable system. Such a system could make medical imaging more accessible, particularly in ambulatory or long-term monitoring settings.

Recent translational efforts in brain-machine interfaces (BMI) are demonstrating the potential to help people with neurological disorders. The current trend in BMI technology is to increase the number of recording channels to the thousands, resulting in the generation of vast amounts of raw data. This in turn places high bandwidth requirements for data transmission, which increases power consumption and thermal dissipation of implanted systems. On-implant compression and/or feature extraction are therefore becoming essential to limiting this increase in bandwidth, but add further power constraints – the power required for data reduction must remain less than the power saved through bandwidth reduction. Spike detection is a common feature extraction technique used for intracortical BMIs. In this paper, we develop a novel firing-rate-based spike detection algorithm that requires no external training and is hardware efficient and therefore ideally suited for real-time applications. Key performance and implementation metrics such as detection accuracy, adaptability in chronic deployment, power consumption, area utilization, and channel scalability are benchmarked against existing methods using various datasets. The algorithm is first validated using a reconfigurable hardware (FPGA) platform and then ported to a digital ASIC implementation in both 65 nm and 0.18MU m CMOS technologies. The 128-channel ASIC design implemented in a 65 nm CMOS technology occupies 0.096 mm2 silicon area and consumes 4.86MU W from a 1.2 V power supply. The adaptive algorithm achieves a 96% spike detection accuracy on a commonly used synthetic dataset, without the need for any prior training.

Multiunit activity (MUA) has been proposed to mitigate the robustness issue faced by single-unit activity (SUA)-based brain-machine interfaces (BMIs). Most MUA-based BMIs still employ a binning method for estimating firing rates and linear decoder for decoding behavioural parameters. The limitations of binning and linear decoder lead to suboptimal performance of MUA-based BMIs. To address this issue, we propose a method which consists of Bayesian adaptive kernel smoother (BAKS) as the firing rate estimation algorithm and deep learning, particularly quasi-recurrent neural network (QRNN), as the decoding algorithm. We evaluated the proposed method for reconstructing (offline) hand kinematics from intracortical neural data chronically recorded from the primary motor cortex of two non-human primates. Extensive empirical results across recording sessions and subjects showed that the proposed method consistently outperforms other combinations of firing rate estimation algorithm and decoding algorithm. Overall results suggest the effectiveness of the proposed method for improving the decoding performance of MUA-based BMIs.

The current trend for implantable Brain Machine Interfaces (BMIs) is to increase the channel count, towards next generation devices that improve on information transfer rate. This however increases the raw data bandwidth for wired or wireless systems that ultimately impacts the power budget (and thermal dissipation). On-implant feature extraction and/or compression are therefore becoming essential to reduce the data rate, however the processing power is of concern. One common feature extraction technique for intracortical BMIs is spike detection. In this work, we have empirically compared the performance, resource utilization, and power consumption of three hardware efficient spike emphasizers, Non-linear Energy Operator (NEO), Amplitude Slope Operator (ASO) and Energy of Derivative (ED), and two common statistical thresholding mechanisms (using mean or median). We also propose a novel median approximation to address the issue of the median operator not being hardware-efficient to implement. These have all been implemented and evaluated on reconfigurable hardware (FPGA) to estimate their hardware efficiency in an ultimate ASIC design. Our results suggest that ED with average thresholding provides the most hardware efficient (low power/resource) choice, while using median has the advantage of improved detection accuracy and higher robustness on threshold multiplier settings. This work is significant because it is the first to implement and compare the hardware and algorithm trade-offs that have to be made before translating the algorithms into hardware instances to design wireless implantable BMIs.

The paper describes the design, implementation, and characterization of a novel multilevel synchronized pulse position modulation paradigm for high efficiency optical biotelemetry links. The entire optoelectronic architecture has been designed with the aim to improve the efficiency of the data transmission and decrease the overall power consumption that are key factors for the fabrication of implantable and wearable medical devices. By employing specially designed digital architectures, the proposed modulation technique automatically transmits more than one bit per symbol together with the reference clock signal enabling the decoding process of the received coded data. In the present case, the paper demonstrates the capability of the modulation technique to transmit symbols composed by 3 and 4 bits. This has been achieved by developing a prototype of an optical biotelemetry system implemented on an FPGA board that, making use of 500 ps laser pulses, operates under the following two working conditions: (i) 40 MHz clock signal corresponding to a baud rate of 40 Mega symbol per second for symbols composed by 3 bits; (ii) 30 MHz clock signal corresponding to a baud rate of 30 Mega symbol per second for symbols composed by 4 bits. Thus, for both these two configurations the transmission data rate is 120 Mbps and the measured BER was lower than 10

Ex vivo preparations enable the study of many neurophysiological processes in isolation from the rest of the body while preserving local tissue structure. This work describes the preparation of rat sciatic nerves for ex vivo neurophysiology, including buffer preparation, animal procedures, equipment setup and neurophysiological recording. This work provides an overview of the different types of experiments possible with this method. The outlined method aims to provide 6 h of stimulation and recording on extracted peripheral nerve tissue in tightly controlled conditions for optimal consistency in results. Results obtained using this method are A-fibre compound action potentials (CAP) with peak-to-peak amplitudes in the millivolt range over the entire duration of the experiment. CAP amplitudes and shapes are consistent and reliable, making them useful to test and compare new electrodes to existing models, or the effects of interventions on the tissue, such as the use of chemicals, surgical alterations, or neuromodulatory stimulation techniques. Both conventional commercially available cuff electrodes with platinum-iridium contacts and custom-made conductive elastomer electrodes were tested and gave similar results in terms of nerve stimulus strength-duration response.

Objective. Translational efforts on spike-signal-based implantable brain-machine interfaces (BMIs) are increasingly aiming to minimise bandwidth while maintaining decoding performance. Developing these BMIs requires advances in neuroscience and electronic technology, as well as using low-complexity spike detection algorithms and high-performance machine learning models. While some state-of-the-art BMI systems jointly design spike detection algorithms and machine learning models, it remains unclear how the detection performance affects decoding. Approach. We propose the co-design of the neural decoder with an ultra-low complexity spike detection algorithm. The detection algorithm is designed to attain a target firing rate, which the decoder uses to modulate the input features preserving statistical invariance in long term (over several months). Main results. We demonstrate a multiplication-free fixed-point spike detection algorithm with an average detection accuracy of 97% across different noise levels on a synthetic dataset and the lowest hardware complexity among studies we have seen. By co-designing the system to incorporate statistically invariant features, we observe significantly improved long-term stability, with decoding accuracy degrading by less than 10% after 80 days of operation. Our analysis also reveals a nonlinear relationship between spike detection and decoding performance. Increasing the detection sensitivity improves decoding accuracy and long-term stability, which means the activity of more neurons is beneficial despite the detection of more noise. Reducing the spike detection sensitivity still provides acceptable decoding accuracy whilst reducing the bandwidth by at least 30%. Significance. Our findings regarding the relationship between spike detection and decoding performance can provide guidance on setting the threshold for spike detection rather than relying on training or trial-and-error. The trade-off between data bandwidth and decoding performance can be effectively managed using appropriate spike detection settings. We demonstrate improved decoding performance by maintaining statistical invariance of input features. We believe this approach can motivate further research focused on improving decoding performance through the manipulation of data itself (based on a hypothesis) rather than using more complex decoding models.

This paper assesses and challenges whether commonly used methods for defining amplitude thresholds for spike detection are optimal. This is achieved through empirical testing of single amplitude thresholds across multiple recordings of varying SNR levels. Our results suggest that the most widely used noise-statistics-driven threshold can suffer from parameter deviation in different noise levels. The spike-noise-driven threshold can be an ideal approach to set the threshold for spike detection, which suffers less from the parameter deviation and is robust to sub-optimal settings.

Mass characterisation of emerging memory devices is an essential step in modelling their behaviour for integration within a standard design flow for existing integrated circuit designers. This work develops a novel characterisation platform for emerging resistive devices with a capacity of up to 1 million devices on-chip. Split into four independent sub-arrays, it contains on-chip column-parallel DACs for fast voltage programming of the DUT. On-chip readout circuits with ADCs are also available for fast read operations covering 5-decades of input current (20nA to 2mA). This allows a device's resistance range to be between 1k$\Omega$ and 10M$\Omega$ with a minimum voltage range of $\pm$1.5V on the device., Comment: 5 pages. To be published in ISCAS 2022 and made available on IEEE Xplore

Over 2 billion people across the world are affected by some visual impairment - mostly related to optical issues, and this number is estimated to grow. Often, particularly in the elderly, more than one condition can affect the eyes at the same time, e.g., myopia and presbyopia. Bifocal or multifocal lenses can be used, these however may become uncomfortable or disturbing and are not adapted to the user. There is therefore a need and opportunity for a new type of glasses able to adaptively change the lenses' focus. This paper explores the feasibility of recording the eye accommodation process in a non-invasive way using a wearable device. This can provide a way to measure eye convergence in real-time to determine what a person's eye is focused on. In this study, Electro-oculography (EoG) is used to observe eye muscle activity and estimate eye movement. To assess this, a group of 11 participants we each asked to switch their gaze from a near to far target and vice versa, whilst their EoG was measured. This revealed two distinct waveforms: one for the transition from a far to near target, and one for the transition from a near to far target. This informed the design of a correlation-based classifier to detect which signals are related to a far to near, or near to far transition. This achieved a classification accuracy of 97.9±1.37% across the experimental results gathered from our 11 participants. This pilot data provides a basic starting point to justify future device development.

Simulations of electroneurogram recording could help find the optimal set of electrodes and algorithms for selective neural recording. However, no flexible methods are established for selective neural recording as for neural stimulation. This paper proposes a method to couple a compartmental and a FEM nerve model, implemented in NEURON and COMSOL, respectively, to translate Node of Ranvier currents into extraneural electric fields. The study simulate ex-vivo experimental conditions, and the method allows flexibility in electrode geometries and nerve topologies. This model has been made available in a public repository

Neuromodulation is an established treatment for numerous neurological conditions, but to expand the therapeutic scope there is a need to improve the spatial, temporal and cell-type specificity of stimulation. Optogenetics is a promising area of current research, enabling optical stimulation of genetically-defined cell types without interfering with concurrent electrical recording for closed-loop control of neural activity. We are developing an open-source system to provide a platform for closed-loop optogenetic neuromodulation, incorporating custom integrated circuitry for recording and stimulation, real-time closed-loop algorithms running on a microcontroller and experimental control via a PC interface. We include commercial components to validate performance, with the ultimate aim of translating this approach to humans. In the meantime our system is flexible and expandable for use in a variety of preclinical neuroscientific applications. The platform consists of a Controlling Abnormal Network Dynamics using Optogenetics (CANDO) Control System (CS) that interfaces with up to four CANDO headstages responsible for electrical recording and optical stimulation through custom CANDO LED optrodes. Control of the hardware, inbuilt algorithms and data acquisition is enabled via the CANDO GUI (Graphical User Interface). Here we describe the design and implementation of this system, and demonstrate how it can be used to modulate neuronal oscillations in vitro and in vivo.

The total economic cost of neurological disorders exceeds £100 billion per annum in the United Kingdom alone, yet pharmaceutical companies continue to cut investments due to failed clinical studies and risk [1]. These challenges motivate an alternative to solely pharmacological treatments. The emerging field of bioelectronics suggests a novel alternative to pharmaceutical intervention that uses electronic hardware to directly stimulate the nervous system with physiologically inspired electrical signals [2]. Given the processing capability of electronics and precise targeting of electrodes, the potential advantages of bioelectronics include specificity in the time, method, and location of treatment, with the ability to iteratively refine and update therapy algorithms in software [3]. A primary disadvantage of the current systems is invasiveness due to surgical implantation of the device.

The introduction of large-scale chemical sensing systems in CMOS which integrate millions of ISFET sensors have allowed applications such as DNA sequencing and fine-pixel chemical imaging systems to be realised. Using CMOS ISFETs provides advantages of digitisation directly at the sensor as well as correcting for non-linearity in its response. However, for this to be beneficial and scale, the readout circuits need to have the minimum possible footprint and power consumption. Within this context, this paper analyses an ISFET based pH-to-time readout using an inverter in the time-domain as a level-crossing detector and presents a 32 × 32 array with in-pixel digitisation for pH sensing. The inverter-based sensing pixel, controlled by a triangular waveform, converts the pH response into a time-domain signal whilst also compensating for sensor offset and thus resulting in an increase in dynamic range. The sensor pixels interface to a 15-bit asynchronous column-wise time-to-digital converter (TDC), enabling fast asynchronous conversion whilst using minimal silicon area. Parallel outputs of 32 TDC interfaces are serialised to achieve fast data throughput. This system is implemented in a standard 0.18 $\,\mu$ m CMOS technology, with a pixel size of 26 $\mu$ m × 26 $\mu$ m and a TDC area of 26 $\mu$ m × 180 $\mu$ m. Additionally, we investigate the use of additional offset compensation by having half of the array implemented with the floating gate tied down via a well diode. Measured results demonstrate the system is able to sense reliably with an average pH sensitivity of 30 mV/pH, whilst being able to compensate for sensor offset by up to $\pm$ 7 V. A resolution of 0.013 pH is achieved and noise measurements show an integrated noise of 0.08 pH within 2–500 Hz and SFDR of 42.6 dB. The total power consumption of the system is measured to be 11.286 mW when operating at a high frame rate of 1 KFPS.

Implantable brain machine interfaces (BMIs) are now on a trajectory to go mainstream, wherein what was once considered last resort will progressively become elective at earlier stages in disease treatment. First-in-human successes have demonstrated the ability to decode highly dexterous motor skills such as handwriting, and speech from human cortical activity. These have been used for cursor and prosthesis control, direct-to-text communication and speech synthesis. Along with these breakthrough studies, technology advancements have enabled the observation of more channels of neural activity through new concepts for centralised/distributed implant architectures. This is complemented by research in flexible substrates, packaging, surgical workflows and data processing. New regulatory guidance and funding has galvanised the field. This culmination of resource, efforts and capability is now attracting significant investment for BMI commercialisation. This paper reviews recent developments and describes the paradigm shift in BMI development that is leading to new innovations, insights and BMI translation.

The unobtrusive monitoring of vital signals and behaviour can be used to gather intelligence to support the care of people living with dementia. This can provide insights into the person's wellbeing and the neurogenerative process, as well as enable them to continue to live safely at home, thereby improving their quality of life. Within this context, this study investigated the deployability of non-contact respiration rate (RR) measurement based on an Ultra-Wideband (UWB) radar System-on-Chip (SoC). An algorithm was developed to simultaneously and continuously extract the respiration signal, together with the confidence level of the respiration signal and the target position, without needing any prior calibration. The radar-measured RR results were compared to the RR results obtained from a ground truth measure based on the breathing sound, and the error rates were within 8% with a mean value of 2.5%. The target localisation results match to the radar-to-chest distances with a mean error rate of 5.8%. The tested measurement range was up to 5m. The results suggest that the algorithm could perform sufficiently well in non-contact stationary respiration rate detection.

This paper describes Tiresias, a low-cost, unobtrusive networked radar system designed to monitor vulnerable patients in domestic environments and provide high quality behavioural and health data. Dementia is a disease that affects millions worldwide and progressively degrades an individual's ability to care for themselves. Eventually most people living with dementia will need to reside in assisted living facilities as they become unable to care for themselves. Understanding the effects dementia has on ability to self-care and extending the length of time people living with dementia can remain living independently are key goals of dementia research and care. The networked radar system proposed in this paper is designed to provide high quality behavioural and health data from domestic environments. This is achieved using multiple radar sensors networked together with their data outputs integrated and processed to produce high confidence measures of position and movement. It is hoped the data produced by this system will both provide insights into how dementia progresses, and also help monitor vulnerable individuals in their own homes, allowing them to remain independent longer than would otherwise be possible.

This paper investigates the relationship between Multi-Unit Activity (MUA) Binning Period (BP) and Brain-Computer Interface (BCI) decoding performance using Long-Short Term Memory decoders. The motivation is to determine whether lossy compression of MUA via increasing BP has any adverse consequences for BCI Behavioral Decoding Performance (BDP). The Neural data originates from intracortical recordings from Macaque Primary Motor cortex [1]. The BDP is measured by the Pearson correlation r between the observed and predicted velocity of the subject’s X-Y hand coordinates in reaching tasks [1]. The results suggest a statistically significant but slight linear relationship between increasing MUA BP and decreasing BDP. For example, when using a 100 ms moving average window, increasing the BP by 10 ms on average reduces the BDP r by approximately 0.85%. This relationship may be due to the reduced number of training examples, or due to the loss of Behavioral information because of reduced MUA temporal resolution.

This paper describes preliminary work towards an automated algorithm for labelling Neuropixel data that exploits the fact that adjacent recording sites are spatially oversampled. This is achieved by combining classical single channel spike sorting with spatial spike grouping, resulting in an improvement in both accuracy and robustness. This is additionally complemented by an automated method for channel selection that determines which channels contain high quality data. The algorithm has been applied to a freely accessible dataset, produced by Cortex Lab, UCL. This has been evaluated to have a accuracy of over 77% compared to a manually curated ground truth.

There is growing interest in using adaptive neuro-modulation to provide a more personalized therapy experience that might improve patient outcomes. Current implant technology, however, can be limited in its adaptive algorithm capability. To enable exploration of adaptive algorithms with chronic implants, we designed and validated the, ‘Picostim DyNeuMo Mk-1’, (DyNeuMo Mk-1 for short), a fully-implantable, adaptive research stimulator that titrates stimulation based on circadian rhythms (e.g. sleep, wake) and the patient’s movement state (e.g. posture, activity, shock, free-fall). The design leverages off-the-shelf consumer technology that provides inertial sensing with low-power, high reliability, and relatively modest cost. The DyNeuMo Mk-1 system was designed, manufactured and verified using ISO 13485 design controls, including ISO 14971 risk management techniques to ensure patient safety, while enabling novel algorithms. The system was validated for an intended use case in movement disorders under an emergency-device authorization from the Medicines and Healthcare Products Regulatory Agency (MHRA). The algorithm configurability and expanded stimulation parameter space allows for a number of applications to be explored in both central and peripheral applications. Intended applications include adaptive stimulation for movement disorders, synchronizing stimulation with circadian patterns, and reacting to transient inertial events such as posture changes, general activity, and walking. With appropriate design controls in place, first-in-human research trials are now being prepared to explore the utility of automated motion-adaptive algorithms.

Memory compilers are necessary tools to boost the design procedure of digital circuits. However, only a few are available to academia. Resistive Random Access Memory (RRAM) is characterised by high density, high speed, non volatility and is a potential candidate of future digital memories. To the best of the authors' knowledge, this paper presents the first open source RRAM compiler for automatic memory generation including its peripheral circuits, verification and timing characterisation. The RRAM compiler is written with Cadence SKILL programming language and is integrated in Cadence environment. The layout verification procedure takes place in Siemens Mentor Calibre tool. The technology used by the compiler is TSMC 180nm. This paper analyses the novel results of a plethora of M x N RRAMs generated by the compiler, up to M = 128, N = 64 and word size B = 16 bits, for clock frequency equal to 12.5 MHz. Finally, the compiler achieves density of up to 0.024 Mb/mm2., Comment: Final Version of NEWCAS 2022. 5 pages

The unobtrusive monitoring of vital signals and behaviour can be used to gather intelligence to support the care of people living with dementia. This can provide insights into the persons wellbeing and the neurogenerative process, as well as enable them to continue to live safely at home, thereby improving their quality of life. Within this context, this study investigated the deployability of non-contact respiration rate (RR) measurement based on an Ultra-Wideband (UWB) radar System-on-Chip (SoC). An algorithm was developed to simultaneously and continuously extract the respiration signal, together with the confidence level of the respiration signal and the target position, without needing any prior calibration. The radar-measured RR results were compared to the RR results obtained from a ground truth measure based on the breathing sound, and the error rates were within 8% with a mean value of 2.4%. The target localisation results match to the radarto-chest distances with a mean error rate of 5.4%. The tested measurement range was up to 5m. The results suggest that the algorithm could perform sufficiently well in non-contact stationary respiration rate detection.

Electrical neurostimulation is effective in treating neurological disorders, but associated recording artefacts generally limit applications to ‘open-loop’ stimuli. Since light does not prevent concurrent electrical recordings, optogenetics enables real-time, continuous ‘closed-loop’ control of brain activity. Here we show that closed-loop optogenetic stimulation with excitatory opsins (CLOSe) affords precise manipulation of neural dynamics, both in vitro, in brain slices from transgenic mice, and in vivo, with anesthetised monkeys. We demonstrate the generation of oscillations in quiescent tissue, enhancement or suppression of endogenous patterns in active tissue, and modulation of seizure-like bursts elicited by 4-aminopyridine. New network properties, emergent under CLOSe, depended on the phase-shift imposed between neural activity and optical stimulation, and could be modelled with a nonlinear dynamical system. In particular, CLOSe could stabilise or destabilise limit cycles associated with seizure oscillations, evident from systematic changes in the variability and entropy of seizure trajectories that correlated with their altered duration and intensity. Furthermore, CLOSe was achieved using intracortical optrodes incorporating light-emitting diodes, paving the way for translation of closed-loop optogenetics towards therapeutic applications in humans.

Here we report on a novel optoelectronic architecture capable to add in-phase the current pulses generated by each one of the fast Si photodiodes forming an array illuminated by laser pulses. The aim is to increase the total photodiode sensitive area to achieve higher output pulsed current values by maintaining unaltered the response time, the bandwidth and the reverse bias voltage proper of each single photodiode. This result is important for many modern applications in biophysics and biomedicine like the brain machine interfaces, that require real time evaluation of environmental/sample changes through the acquisition of signals at high data rates under high signal-to-noise ratio conditions. As a consequence, these applications use sub-nanosecond laser pulses that are revealed by large bandwidth photodiodes having small sensitive area to attain low values of the internal junction capacitance. Thus, any small optical misalignment between the laser beam and the photodiode sensitive area in transcutaneous optical biotelemetry strongly decreases the system detection efficiency and performances. The proposed optoelectronic system resolves this problem being capable to sum in-phase the current pulses generated by each one of the photodiodes forming the array into a single current pulse maintaining the condition of fast response in terms of rise and fall times and bandwidth. This solution employs a Kirchhoff node to sum the different photocurrents, a decoupling current buffer and a transimpedance amplifier to amplify the current pulses summed at the node. We investigated the performances of the optoelectronic system by using photodiodes with different sensitive areas and junction capacitances (i.e., different bandwidths and rise and fall times) for laser pulses repetition rates up to 200 MHz. We also experimentally characterized the circuitry composed by an array of 4 fast photodiodes by using 800 ps laser pulses at a repetition rate of 200 MHz proving that the achieved response times and bandwidth remain the same of those ones of each single photodiode. Moreover, we demonstrated that the maximum value (i.e., the peak) of the obtained output current pulses is multiplied by a factor 4, i.e., equal to the number of the photodiodes forming the array having an overall sensitive area enhanced by the same factor.

There is growing interest in using adaptive neuro-modulation to provide a more personalized therapy experience that might improve patient outcomes. Current implant technology, however, can be limited in its adaptive algorithm capability. To enable exploration of adaptive algorithms with chronic implants, we designed and validated the ‘Picostim DyNeuMo Mk-1’ (DyNeuMo Mk-1 for short), a fully-implantable, adaptive research stimulator that titrates stimulation based on circadian rhythms (e.g. sleep, wake) and the patient’s movement state (e.g. posture, activity, shock, free-fall). The design leverages off-the-shelf consumer technology that provides inertial sensing with low-power, high reliability, and relatively modest cost. The DyNeuMo Mk-1 system was designed, manufactured and verified using ISO 13485 design controls, including ISO 14971 risk management techniques to ensure patient safety, while enabling novel algorithms. The system was validated for an intended use case in movement disorders under an emergency-device authorization from the Medicines and Healthcare Products Regulatory Agency (MHRA). The algorithm configurability and expanded stimulation parameter space allows for a number of applications to be explored in both central and peripheral applications. Intended applications include adaptive stimulation for movement disorders, synchronizing stimulation with circadian patterns, and reacting to transient inertial events such as posture changes, general activity, and walking. With appropriate design controls in place, first-in-human research trials are now being prepared to explore the utility of automated motion-adaptive algorithms.

Animals undertake specific behaviors before sleep. Little is known about whether these innate behaviors, such as nest building, are actually an intrinsic part of the sleep-inducing circuitry. We found, using activity-tagging genetics, that mouse prefrontal cortex (PFC) somatostatin/GABAergic (SOM/GABA) neurons, which become activated during sleep deprivation, induce nest building when opto-activated. These tagged neurons induce sustained global NREM sleep if their activation is prolonged metabotropically. Sleep-deprivation-tagged PFC SOM/GABA neurons have long-range projections to the lateral preoptic (LPO) and lateral hypothalamus (LH). Local activation of tagged PFC SOM/GABA terminals in LPO and the LH induced nesting and NREM sleep respectively. Our findings provide a circuit link for how the PFC responds to sleep deprivation by coordinating sleep preparatory behavior and subsequent sleep.

This paper investigates to what extent Long Short-Term Memory (LSTM) decoders can use Local Field Potentials (LFPs) to predict Single-Unit Activity (SUA) in Macaque Primary Motor cortex. The motivation is to determine to what degree the LFP signal can be used as a proxy for SUA, for both neuroscience and Brain-Computer Interface (BCI) applications. Firstly, the results suggest that the prediction quality varies significantly by implant location or animal. However, within each implant location / animal, the prediction quality seems to be correlated with the amount of power in certain LFP frequency bands (0-10, 10-20 and 40-50Hz, standardised LFPs). Secondly, the results suggest that bipolar LFPs are more informative as to SUA than unipolar LFPs. This suggests common mode rejection aids in the elimination of non-local neural information. Thirdly, the best individual bipolar LFPs generally perform better than when using all available unipolar LFPs. This suggests that LFP channel selection may be a simple but effective means of lossy data compression in Wireless Intracortical LFP-based BCIs. Overall, LFPs were moderately predictive of SUA, and improvements can likely be made.

Multiunit activity (MUA) has been proposed to mitigate the robustness issue faced by single-unit activity (SUA)-based brain-machine interfaces (BMIs). Most MUA-based BMIs still employ a binning method for extracting firing rates and linear decoder for decoding behavioural parameters. The limitations of binning and linear decoder lead to suboptimal performance of MUA-based BMIs. To address this issue, we propose Bayesian adaptive kernel smoother (BAKS) as the feature extraction method and long short-term memory (LSTM)-based deep learning as the decoding algorithm. We evaluated the proposed methods for reconstructing (offline) hand kinematics from intracortical neural data chronically recorded from the motor cortex of a monkey. Experimental results showed that BAKS coupled with LSTM outperformed other combinations of feature extraction method (binning or fixed kernel smoother) and decoding algorithm (Kalman filter or Wiener filter). Overall results demonstrate the effectiveness of BAKS and LSTM for improving the decoding performance of MUA-based BMIs.

Extracellular recordings are typically analysed by separating them into two distinct signals: local field potentials (LFPs) and spikes. Understanding the relationship between these two signals is essential for gaining deeper insight into neuronal coding and information processing in the brain and is also relevant to brain-machine interface (BMI) research. Previous studies have shown that spikes, in the form of single-unit activity (SUA) or multiunit activity (MUA), can be inferred solely from LFPs with moderately good accuracy. These spiking activities that are typically extracted via threshold-based technique may not be reliable when the recordings exhibit a low signal-to-noise ratio (SNR). Another spiking activity in the form of a continuous signal, referred to as entire spiking activity (ESA), can be extracted by a threshold-less, fast, and automated technique and has led to better performance in several tasks. However, its relationship with the LFPs has not been investigated. In this study, we aim to address this issue by employing a deep learning method to infer ESA from LFPs intracortically recorded from the motor cortex area of two monkeys performing different tasks. Results from long-term recording sessions and across different tasks revealed that the inference accuracy of ESA yielded consistently and significantly higher accuracy than that of SUA and MUA. In addition, local motor potential (LMP) was found to be the most highly predictive feature compared to other LFP features. The overall results indicate that LFPs contain substantial information about the spikes, particularly ESA, which could be useful for the development of LFP-based BMIs. The results also suggest the potential use of ESA as an alternative neuronal population activity measure for analysing neural responses to stimuli or behavioural tasks.

This paper investigates the effectiveness of four Huffman-based compression schemes for different intracortical neural signals and sample resolutions. The motivation is to find effective lossless, low-complexity data compression schemes for Wireless Intracortical Brain-Machine Interfaces (WI-BMI). The considered schemes include pre-trained Lone 1st and 2nd order encoding [1], pre-trained Delta encoding, and pre-trained Linear Neural Network Time (LNNT) encoding [2]. Maximum codeword-length limited versions are also considered to protect against overfit to training data. The considered signals are the Extracellular Action Potential signal, the Entire Spiking Activity signal, and the Local Field Potential signal. Sample resolutions of 5 to 13 bits are considered. The result show that overfit-protection dramatically improves compression, especially at higher sample resolutions. Across signals, 2nd order encoding generally performed best at lower sample resolutions, and 1st order, Delta and LNNT encoding performed best at higher sample resolutions. The proposed methods should generalise to other remote sensing applications where the distribution of the sensed data can be estimated a priori.

Brain-machine Interfaces (BMI) hold great potential for treating neurological disorders such as epilepsy. Technological progress is allowing for a shift from open-loop, pacemaker-class, intervention towards fully closed-loop neural control systems. Low power programmable processing systems are therefore required which can operate within the thermal window of 2° C for medical implants and maintain long battery life. In this work, we have developed a low power neural engine with an optimized set of algorithms which can operate under a power cycling domain. We have integrated our system with a custom-designed brain implant chip and demonstrated the operational applicability to the closed-loop modulating neural activities in in-vitro and in-vivo brain tissues: the local field potentials can be modulated at required central frequency ranges. Also, both a freely-moving non-human primate (24-hour) and a rodent (1-hour) in-vivo experiments were performed to show system reliable recording performance. The overall system consumes only 2.93 mA during operation with a biological recording frequency 50 Hz sampling rate (the lifespan is approximately 56 hours). A library of algorithms has been implemented in terms of detection, suppression and optical intervention to allow for exploratory applications in different neurological disorders. Thermal experiments demonstrated that operation creates minimal heating as well as battery performance exceeding 24 hours on a freely moving rodent. Therefore, this technology shows great capabilities for both neuroscience in-vitro/in-vivo applications and medical implantable processing units.

This article reports an implantable transcutaneous telemetry for a brain machine interface that uses a novel optical communication system to achieve a highly energy-efficient link. Based on an pulse-based coding scheme, the system uses sub-nanosecond laser pulses to achieve data rates up to 300 Mbps with relatively low power levels when compared to other methods of wireless communication. This has been implemented using a combination of discrete components (semiconductor laser and driver, fast-response Si photodiode and interface) integrated at board level together with reconfigurable logic (encoder, decoder and processing circuits implemented using Xilinx KCU105 board with Kintex UltraScale FPGA). Experimental validation has been performed using a tissue sample that achieves representative level of attenuation/scattering (porcine skin) in the optical path. Results reveal that the system can operate at data rates up to 300 Mbps with a bit error rate (BER) of less than 10

Within the rapidly advancing field of active implantable medical devices, power management is a major consideration. Devices that provide life critical (or avoiding life threatening) function require a dependable, always-on power source, for example pacemakers. There is then a trade-off with battery lifetime as to whether such devices employ a primary cell or rechargeable battery. With new applications requiring multi-module implants, there is now also a need for transmitting within the body from one device to another. This paper outlines the key considerations and the process to define and optimise the power management strategy. We then apply this to a case study application – developing an implanted, multi-module closed-loop neuromodulation device for the treatment of focal epilepsy.

Next generation implantable neural interfaces are targeting devices with mm-scale form factors that are freely floating and completely wireless. Scalability to more recording (or stimulation) channels will be achieved through distributing multiple devices, instead of the current approach that uses a single centralized implant wired to individual electrodes or arrays. In this way, challenges associated with tethers, micromotion, and reliability of wiring is mitigated. This concept is now being applied to both central and peripheral nervous system interfaces. One key requirement, however, is to maximize specific absorption rate (SAR) constrained achievable wireless power transfer efficiency (PTE) of these inductive links with mm-sized receivers. Chip-scale coil structures for microsystem integration that can provide efficient near-field coupling are investigated. We develop near-optimal geometries for three specific coil structures: in-CMOS, above-CMOS (planar coil post-fabricated on a substrate), and around-CMOS (helical wirewound coil around substrate). We develop analytical and simulation models that have been validated in air and biological tissues by fabrications and experimental measurements. Specifically, we prototype structures that are constrained to a 4 mm $ \times$ 4 mm silicon substrate, i.e., the planar in-/above-CMOS coils have outer diameters $ 4 mm, whereas the around-CMOS coil has an inner diameter of 4 mm. The in-CMOS and above-CMOS coils have metal film thicknesses of 3- $\mu$ m aluminium and 25- $\mu$ m gold, respectively, whereas the around-CMOS coil is fabricated by winding a 25- $\mu$ m gold bonding wire around the substrate. The measured quality factors ( Q ) of the mm-scale Rx coils are 10.5 @450.3 MHz (in-CMOS), 24.61 @85 MHz (above-CMOS), and 26.23 @283 MHz (around-CMOS). Also, PTE of 2-coil links based on three types of chip-scale coils is measured in air and tissue environment to demonstrate tissue loss for bio-implants. The SAR-constrained maximum PTE measured (together with resonant frequencies, in tissue) are 1.64% @355.8 MHz (in-CMOS), 2.09% @82.9 MHz (above-CMOS), and 3.05% @318.8 MHz (around-CMOS).

This paper presents a low-power analog-to-time converter (ATC) for integrated bio-sensors. The proposed circuit facilitates the direct conversion of electrode bio-potential recordings into time-encoded digital pulses with high efficiency without prior signal amplification. This approach reduces the circuit complexity for multi-channel instrumentation systems and allows asynchronous digital control to maximize the potential power savings during sensor inactivity. A prototype fabricated using a 65-nm CMOS technology is demonstrated with measured characteristics. Experimental results show an input-referred noise figure of 3.8 $\mu V_{\mathrm{ rms}}$ for a 11-kHz signal bandwidth while dissipating 1.2 $\mu \text{W}$ from a 0.5-V supply and occupying $60\times 80\,\,\mu \text{m}\,\,^{\mathrm{ 2}}$ silicon area. This compact configuration is enabled by the proposed asynchronous readout that shapes the mismatch components arising from the multi-bit quantizer and the use of capacitive feedback.

The instrumentation systems for implantable brain–machine interfaces represent one of the most demanding applications for ultra low-power analogue-to-digital-converters (ADC) to date. To address this challenge, this paper proposes a $\Delta \Sigma $ SAR topology for very large sensor arrays that allows an exceptional reduction in silicon footprint by using a continuous time 0–2MASH topology. This configuration uses a specialized FIR window to decimate the $\Delta \Sigma $ modulator output and reject mismatch errors from the SAR quantizer, which mitigates the overhead from dynamic element matching techniques commonly used to achieve high precision. A fully differential prototype was fabricated using $0.18\,\mu $ m CMOS to demonstrate 10.8 ENOB precision with a 0.016 mm2 silicon footprint. Moreover, a 14fJ/conv figure-of-merit can be achieved, while resolving signals with the maximum input amplitude of ±1.2 Vpp sampled at 200 kS/s. The ADC topology exhibits a number of promising characteristics for both high speed and ultra low-power systems due to the reduced complexity, switching noise, sampling load, and oversampling ratio, which are critical parameters for many sensor applications.

Heart rate variability (HRV), as measured by ultra-wideband (UWB) radar, enables contactless monitoring of physiological functioning in the human body. In the current study, we verified the reliability of HRV extraction from radar data, under limited transmitter power. In addition, we conducted a feasibility study of mental state classification from HRV data, measured using radar. Specifically, arctangent demodulation with calibration and low rank approximation have been used for radar signal pre-processing. An adaptive continuous wavelet filter and moving average filter were utilized for HRV extraction. For the mental state classification task, performance of support vector machine, k-nearest neighbors and random forest classifiers have been compared. The developed system has been validated on human participants, with 10 participants for HRV extraction, and three participants for the proof-of-concept mental state classification study. The results of HRV extraction demonstrate the reliability of time-domain parameter extraction from radar data. However, frequency-domain HRV parameters proved to be unreliable under low SNR. The best average overall mental state classification accuracy achieved was 82.34%, which has important implications for the feasibility of mental health monitoring using UWB radar.

The ability to acquire realtime diagnostics of brain tissue intraoperatively represents a key goal in the field of brain tumour neurosurgery. This can greatly enhance the precision, extent and effectiveness of key surgical procedures such as those performed for brain tumour resection and biopsy. To achieve this requires a miniature, handheld tool which can perform intraoperative in situ, in-vivo characterisation of different types of tissues e.g. normal brain tissue versus tumour tissue. Here we explored the feasibility and requirements of implementing a portable impedance characterisation system for brain tumour detection. We proposed and implemented a novel system based on PCB-based instrumentation using a square four-electrode microendoscopic probe. The system uses a digital-to-analogue converter to generate a multi-tone sinusoid waveform, and a floating bi-directional voltage-to-current converter to output the differential stimulation current to one pair of electrodes. The other pair of electrodes are connected to the sensing circuit based on an instrumentation amplifier. The recorded data is pre-processed by the micro-controller and then analysed on a host computer. To evaluate the system, tetrapolar impedances have been recorded from a number of different electrode configurations to sense pre-defined resistance values. The overall system consumed 143mA current, achieved 0.1% linearity and 15µV noise level, with a maximum signal bandwidth of 100kHz. Initial experimental results on tissue were carried out on a piece of rib-eye steak. Electrical impedance maps (EIM) and contour plots were then reconstructed to represent the impedance value in different tissue region.

The recent evolution of implantable medical devices from single-unit stimulators to modern implantable microsystems, has driven the need for distributed technologies, in which both the implant system and functions are partitioned across multiple active devices. This multi-module approach is made possible thanks to novel network architectures, allowing for in-body power and data communications to be performed using implantable leads. This paper discusses the challenges in implementing such interfacing system and presents a platform based on one central implant (CI) and multiple peripheral implants (PIs) using a custom 4WiCS communication protocol. This is implemented in PCB technology and tested to demonstrate intrabody communication capabilities and power transfer within the network. Measured results show CI-to-PI power delivery achieves 70 % efficiency in expected load condition, while establishing full-duplex data link with up to 4 PIs simultaneously.

This paper presents a robust EM-lens-enhanced wireless power transmission system and a novel multiple-node data communication method for distributed implantable medical devices. The proposed techniques can solve the common issues caused by multiple implanted devices, such as low power transfer efficiency through biological tissues, uneven delivered power for distributed devices, interference between simultaneous wireless power and data transmission, and intermodulation distortion between various data channels. A prototype system has been designed and manufactured with discrete components on FR4 substrate as a proof of concept. The EM-Lens-enhanced inductive links can expand the power coverage of transmitting (Tx) coil from 9mm×5mm to 14mm×13mm, and double the recovered DC voltage from 1.8V to 3.2V at 12.5mm distance. Data communication is achieved by novel low-power backscatter-CDMA scheme. This permits transmission of data from several nodes all operating with the same carrier frequency simultaneously reflecting the power carriers to the primary side. In this paper, we demonstrate simultaneous communication between two nodes at 125kbps with 1.05mW power consumption.

Neural interfaces, and more specifically ones of the invasive/implantable variety, today are a topic of much controversy, often making the general public uncomfortable and intimidated. We have thus devised a bespoke interactive demo to help people understand brain implants and their need in the age of wearable devices, with the secondary objective of introducing the wireless cortical neural probe that we, at NGNI (Next Generation Neural Interfaces) lab, are developing.

In recent years, local field potentials (LFPs) have emerged as a promising alternative input signal for brain-machine interfaces (BMIs). Several studies have demonstrated that LFP-based BMIs could provide long-term recording stability and, at the same time, comparable decoding performance to their spike counterparts. However, despite the compelling results, most LFP-based BMIs still make use of hand-crafted features which is a time-consuming process and can be suboptimal. In this paper, we propose an end-to-end system approach based on temporal convolutional network (TCN) to automatically extract features and decode kinematics of hand movements directly from raw LFP signals. We benchmark its decoding performance against traditional approaches incorporating long short-term memory (LSTM) decoders driven by hand-crafted LFP features. Experimental results demonstrate significant performance improvement of the proposed approach compared to the traditional approaches, demonstrating the suitability and the potential of TCN-based end-to-end systems in providing stable and high decoding performance LFP-based BMIs.

Sleep serves an underlying function in mammals that appears to be essential for life, but despite decades of research, this function and a large part of the neuronal circuitry underlying its control, are little understood. To conduct research in this field, many devices capable of recording neural signals such as LFP and EEG have been developed. However, due to their size and weight, most of these devices are unsuitable for sleep studies in mice, the most commonly used animals. This paper presents a novel 4 channel, compact (2.1cm by 1.7cm) and lightweight (3.6g) neural-logging device that can record for 3 days on just two 0.6g zinc air 312 batteries. Instead of the typical solution of using multiple platforms, the presented device integrates high resolution EEG, EMG and temperature recordings into one platform. The onboard BLE module allows the device to be controlled wirelessly as well as stream data in real time, enabling researchers to check the progress of the recording with minimal animal disturbance. The device is able to accurately record EEG and temperature data through long 24 hour in-vivo recordings conducted. The obtained EEG data could be easily sleep scored and the temperature values were all within the expected physiological range.

Coherent ultra-wideband (UWB) radar-on-chip technology shows great promise for developing portable and low-cost medical imaging and monitoring devices. Particularly monitoring the mechanical functioning of the cardiovascular system is of interest, due to the ability of radar systems to track sub-mm motion inside the body at a high speed. For imaging applications, UWB radar systems are required, but there are still significant challenges with in-body sensing using low-power microwave equipment and wideband signals. Recently it was shown for the first time, on a single subject, that the arterial pulse wave can be measured at various locations in the body, using coherent UWB radar-on-chip technology. The current work provides more substantial evidence, in the form of new measurements and improved methods, to demonstrate that cardiovascular dynamics can be measured using radar-on-chip. Results across four participants were found to be robust and repeatable. Cardiovascular signals were recorded using radar-on-chip systems and electrocardiography (ECG). Through ECG-aligned averaging, the arterial pulse wave could be measured at a number of locations in the body. Pulse arrival time could be determined with high precision, and blood pressure pulse wave propagation through different arteries was demonstrated. In addition, cardiac dynamics were measured from the chest. This work serves as a first step in developing a portable and low-cost device for long-term monitoring of the cardiovascular system, and provides the fundamentals necessary for developing UWB radar-on-chip imaging systems.