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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.

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.

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 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.

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.

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.

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.

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.

This paper describes an on-chip interface for recovering power and providing full-duplex communication over an AC-coupled 4-wire lead between active implantable devices. The target application requires two modules to be implanted in the brain (cortex) and upper chest; connected via a subcutaneous lead. The brain implant consists of multiple identical “optrodes” that facilitate a bidirectional neural interface (electrical recording and optical stimulation), and the chest implant contains the power source (battery) and processor module. The proposed interface is integrated within each optrode ASIC allowing full-duplex and fully-differential communication based on Manchester encoding. The system features a head-to-chest uplink data rate (up to 1.6 Mbps) that is higher than that of the chest-to-head downlink (100 kbps), which is superimposed on a power carrier. On-chip power management provides an unregulated 5-V dc supply with up to 2.5-mA output current for stimulation, and two regulated voltages (3.3 and 3 V) with 60-dB power supply rejection ratio for recording and logic circuits. The 4-wire ASIC has been implemented in a 0.35-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mu \text{m}$ \end{document} CMOS technology, occup-ying a 1.5-mm2 silicon area, and consumes a quiescent current of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$91.2~\mu \text{A}$ \end{document}. The system allows power transmission with measured efficiency of up to 66% from the chest to the brain implant. The downlink and uplink communication are successfully tested in a system with two optrodes and through a 4-wire implantable lead.

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.

This paper presents a novel analogue to digital converter using an oscillator-based loop filter for high-dynamic range bio-sensing applications. This is the first third-order feedforward ΔΣ modulator that strictly uses time domain integration for quantisation noise shaping. Furthermore we propose a new asynchronous extended-phase detection technique that increases the resolution of the 4 bit phase quantiser by another 5 bits to significantly improve both dynamic range and reduce the noise-shaping requirements. Preliminary simulation results show that this type of loop-filter can virtually prevent integrator saturation and achieves a peak 88 dB SNDR for kHz signals. The proposed system has been implemented using a 180 nm CMOS technology occupying 0.102 mm 2 and consumes 13.7 μW of power to digitise the 15 kHz signal bandwidth using a 2 MHz sampling clock.

This paper presents an oscillator based potentiostat with switch-cap feedback for current sensing applications. In this work, a sigma-delta modulator is realized using current as input and charge as the feedback. The current input is integrated at the input capacitance and the voltage difference between the integrated value and reference input is converted to current via a transconductor. This current is then fed into a current controlled ring oscillator and the frequency output drives the switch-cap circuits to compensate the input current. Therefore, the current input can be directly readout as frequency deviation, or can be further converted to digital output via a frequency to digital converter. The proposed system is implemented in a typical 0.18µm CMOS technology, with total area of 80 × 250µm2. A differential structure was implemented to minimize the parasitic and kick back influence, with oscillator base frequency at 491MHz. A counter based frequency to digital converter with an additional CIC filter was implemented to convert the differential frequency signal to digital domains at 16MHz sampling frequency. Simulation results demonstrated that a dynamic range of 52dB was achieved with input range of ±2.5µA.

This paper presents a 17 bit analogue-to-digital converter that incorporates mismatch and quantisation noise-shaping techniques into an energy-saving 10 bit successive approximation quantiser to increase the dynamic range by another 42 dB. We propose a novel fully-capacitive topology which allows for high-speed asynchronous conversion together with a background calibration scheme to reduce the oversampling requirement by 10× compared to prior-art. A 0.18μm CMOS technology is used to demonstrate preliminary simulation results together with analytic measures that optimise parameter and topology selection. The proposed system is able to achieve a FoM S of 183 dB for a maximum signal bandwidth of 15.6 kHz while dissipating 68 μW from a 1.8 V supply. A peak SNDR of 102 dB is demonstrated for this rate with a 0.201 mm2 area requirement.

This paper presents a 32×32 ISFET sensing array with in-pixel digitisation for pH sensing. The in-pixel digitisation is achieved using an inverter-based sensing pixel that is controlled by a triangular waveform. This converts the pH response of the ISFET into a time-domain signal whilst also increasing dynamic range and thus the ability to tolerate sensor offset. The pixels are interfaced to a 15-bit asynchronous column-wise time-to-digital converter (TDC), enabling fast sensor readout whilst using minimal silicon area. Parallel output of 32 TDC interfaces are serialised to achieve fast data though-put. This system is implemented in a standard 0.18 μm standard CMOS technology, with a pixel size of 26 μm × 26 μm and a TDC of 26 μm × 180 μm. Simulation results demonstrate that chemical sampling of up to 5k frames per second can be achieved with a clock frequency of 160 MHz and a TDC resolution of 190 ps. The total power consumption of the overall system is 7.34 mW.

Brain machine interfaces notoriously face difficulties in achieving long term implanted recording stability. It has been shown that damage and inflammation, caused during insertion by electrodes that are too large and stiff, provoke a sustained inflammatory tissue response. This is commonly referred to as the foreign body response, resulting in encapsulation and thus increased electrode impedance over time. Accordingly, neural interfaces with ever smaller and more flexible electrodes are continually in development, but unfortunately face challenges of their own, first and foremost of which is buckling and bending during insertion. This work presents the development of a prototype insertion method, comprising an insertion device and novel probe architecture, that promotes straight insertion without buckling, while simultaneously minimizing the insertion force for multi-microwire electrode probes. When compared against insertion of probes with unsupported free electrodes, the prototype method achieved significantly straighter electrode insertion, resulting in both a smaller distance between electrode recording tips and a greater average insertion depth. While achieving less straight insertion than probes with sucrose coated electrodes, a common technique for promoting reliable insertion without buckling, the tested method was able to maintain significantly lower insertion forces.

This paper presents a novel wireless power transfer (WPT) scheme that consists of a two-tier hierarchy of near-field inductively coupled links. Key aims are to provide efficient power transfer efficiency (PTE) and uniform energy distribution for mm-scale free-positioned neural implants. The top tier facilitates a transcutaneous link from a scalp-worn (em-scale) primary coil to a subcutaneous array of smaller, parallel-connected secondary coils. These are then wired through the skull to a corresponding set of parallel connected primary coils in the lower tier, placed epidurally. These then inductively couple to freely positioned (mm-scale) secondary coils within each subdural implant. This architecture has three key advantages: (1) the opportunity to achieve efficient energy transfer by utilising two short-distance inductive links; (2) good uniformity of the transdural power distribution through the multiple (redundant) coils; and (3) a reduced risk of infection by maintaining the dura protecting the blood-brain barrier. The functionality of this approach has been verified and optimised through HFSS simulations, to demonstrate the robustness against positional and angular misalignment. The average 11.9% PTE and 26.6% power distribution deviation (PDD) have been achieved for the Rx coil in parallel with the epidural coil array. The average PTE and PDD degenerate to 2.6% and 62.8% when the Rx coil is perpendicular to the epidural coil array.

This paper presents a compact direct digital wavelet synthesizer for extracting phase and amplitude data from cortical recordings using a feed-forward recurrent digital oscillator. These measurements are essential for accurately decoding local- field-potentials in selected frequency bands. Current systems extensively to rely large digital cores to efficiently perform Fourier or wavelet transforms which is not viable for many implants. The proposed system dynamically controls oscillation to generate frequency selective quadrature wavelets instead of using memory intensive sinusoid/cordic look-up-tables while retaining robust digital operation. A MachXO3LF Lattice FPGA is used to present the results for a 16 bit implementation. This configuration requires 401 registers combined with 283 logic elements and also accommodates real-time reconfigurability to allow ultra-low- power sensors to perform spectroscopy with high-fidelity.

This paper presents a novel chopping method allow- ing suppression of 1/f flicker noise in continuous-time acquisition systems without the need for a fixed-frequency clock, stochasti- cally deriving the chopping signal from the input and hence achieving completely signal-dependent power consumption. The method is analysed, its basis of operation explained and a proof- of-concept implementation presented alongside simulated results demonstrating an increase in achieved SNR of more than 8 dB during acquisition of ECG, EAP and EEG signals.

Recent developments in the design of active im- plantable devices have achieved significant advances, for example, an increased number of recording channels, but too often practical clinical applications are restricted by device longevity. It is important however to complement efforts for increased func- tionality with translational work to develop implant technologies that are safe and reliable to be hosted inside the human body over long periods of time. This paper first examines techniques currently used to evaluate micro-package hermeticity and key challenges, highlighting the need for new, in situ instrumentation that can monitor the encapsulation status over time. Two novel circuits are then proposed to tackle the specific issue of moisture penetration inside a sub-mm, silicon-based package. They both share the use of metal tracks on the different layers of the CMOS stack to measure changes in impedance caused by moisture present in leak cracks or diffused into the oxide layers.

Deep Brain Stimulation (DBS) is a widely used clin- ical therapeutic modality to treat Parkinsons disease refractory symptoms and complications of levodopa therapy. Currently available DBS systems use continuous, open-loop stimulation strategies. It might be redundant and we could extend the battery life otherwise. Recently, robust electrophysiological signatures of Parkinsons disease have been characterized in motor cortex of patients undergoing DBS surgery. Reductions in the beta- gamma Phase-Amplitude coupling (PAC) correlated with symp- tom improvement, and the therapeutic effects of DBS itself. We aim to develop a miniature, implantable and adaptive system, which only stimulates the neural target, when triggered by the output of the appropriate PAC algorithm. As a first step, in this paper we compare published PAC algorithms by using human data intra-operatively recorded from Parkinsonian patients. We then introduce IIR masking for later achieving fast and low- power FPGA implementation of PAC mapping for intra-operative studies. Our closed-loop application is expected to consume significantly less power than current DBS systems, therefore we can increase the battery life, without compromising clinical benefits.

Single-chip UWB radar systems have enormous potential for the development of portable, low-cost and easy-to-use devices for monitoring the cardiovascular system. Using body coupled antennas, electromagnetic energy can be directed into the body to measure arterial pulsation and cardiac motion, and estimate arterial stiffness as well as blood pressure. In the current study we validate that heart rate signals, obtained using multiple UWB radar-on-chip modules and body coupled antennas, do indeed originate from arterial pulsation. Through ECG-aligned averaging, pulse arrival time at a number of locations in the body could be measured with high precision, and arterial pulse propagation through the femoral and carotid artery was demonstrated. In addition, cardiac dynamics were measured from the chest. Onset and offset of ventricular systole were clearly distinguishable, as well as onset of atrial systole. Although further validation is required, these results show that UWB radar-on-chip is highly suitable for monitoring of vascular health as well as the heart's mechanical functioning.

Implantable neural interfaces have evolved in the past decades from stimulation-only devices to closed-loop record- ing and stimulation systems, allowing both for more targeted therapeutic techniques and more advanced prosthetic implants. Emerging applications require multi-module active implantable devices with intrabody power and data transmission. This distributed approach poses a new set of challenges related to inter-module connectivity, functional reliability and patient safety. This paper addresses the ground referencing challenge in active multi-implant systems, with a particular focus on neural recording devices. Three different grounding schemes (passive, drive, and sense) are presented and evaluated in terms of both recording reliability and patient safety. Considerations on the practical implementation of body potential referencing circuitry are finally discussed, with a detailed analysis of their impact on the recording performance.

This paper reports on the implementation and characterisation of a thermally controlled device for in vitro biomedical applications, based on standard Printed Circuit Board (PCB) technology. This is proposed as a low cost alternative to state-of-the-art microfluidic devices and Lab-on-Chip (LoC) platforms, which we refer to as the thermal Lab-on-PCB concept. In total, six different prototype boards have been manufactured to implement as many mini-hotplate arrays. 3D multiphysics software simulations show the thermal response of the modelled mini-hotplate boards to electrical current stimulation, highlight- ing their versatile heating capability. A comparison with the results obtained by the characterisation of the fabricated PCBs demonstrates the dual temperature sensing/heating property of the mini-hotplate, exploitable in a larger range of temperature with respect to the typical operating range of LoC devices. The thermal system is controllable by means of external off-the-shelf circuitry designed and implemented on a single-channel control board prototype.

High-Frequency alternating current nerve block has great potential for neuromodulation-based therapies. However, no precise measurements have been made of the time needed for nerves to recover from block once the signal has been turned off. This study aims to characterise time to recovery of the rat sciatic nerve after 30 seconds of block at varying amplitudes and frequencies. Experiments were carried out in-vivo to quantify recovery times and recovery completeness within 0.7s from the end of block. The sciatic nerve was blocked with an alternating square wave signal of amplitude and frequency ranging from 2 to 9mA and 10 to 50 kHz respectively. To determine the recovery dynamics the nerve was stimulated at 100 Hz after cessation of the blocking stimulus. Electromyogram signals were measured from the gastrocnemius medialis and tibialis anterior muscles during trials as indicators of nerve function. This allowed for nerve recovery to be measured with a resolution of 10 ms. This resolution is much greater than previous measurements of nerve recovery in the literature. Times for the nerve to recover to a steady state of activity ranged from 20 to 430 milliseconds and final relative recovery activity at 0.7 seconds spanned 0.2 to 1 approximately. Higher blocking signal amplitudes increased recovery time and decreased recovery completeness. These results suggest that blocking signal properties affect nerve recovery dynamics, which could help improve neuromodulation therapies and allow more precise comparison of results across studies using different blocking signal parameters.

With modern microtechnology, there is an aggressive miniaturization of smart devices, despite an increasing level of integration and overall complexity. It is therefore becoming increasingly important to be achieve reliable, compact packaging. For implantable medical devices (IMDs), the package must additionally provide a high quality hermetic environment to protect the device from the human body. For chip-scale devices, AuSn eutectic bonding offers the possibility of forming compact seals that achieve ultra-low permeability. A key feature is this can be achieved at process temperatures of below 350315°C, therefore allowing for the integration of sensors and microsystems with CMOS electronics within a single package. Issues however such as solder wetting, void formation and controlling composition make formation of high-quality repeatable seals highly challenging. Towards this aim, this paper presents our experimental work characterizing the eutectic stack deposition. We detail our designmethods and process flow, share our experiences in controlling electrochemical deposition of AuSn alloy and finally discuss usability of sequential electroplating process for the formation of hermetic eutectic bonds.

Next generation brain machine interfaces fundamentally need to improve the information transfer rate and chronic consistency when observing neural activity over a long period of time. Towards this aim, this paper presents a novel System-on-Chip (SoC) for a mm-scale wireless neural recording node that can be implanted in a distributed fashion. The proposed self-regulating architecture allows each implant to operate autonomously and adaptively load the electromagnetic field to extract a precise amount of power for full-system operation. This can allow for a large number of recording sites across multiple implants extending through cortical regions without increased control overhead in the external head-stage. By observing local field potentials (LFPs) only, chronic stability is improved and good coverage is achieved whilst reducing the spatial density of recording sites. The system features a ΔΣ based instrumentation circuit that digitises high fidelity signal features at the sensor interface thereby minimising analogue resource requirements while maintaining exceptional noise efficiency. This has been implemented in a 0.35 μm CMOS technology allowing for wafer-scale post-processing for integration of electrodes, RF coil, electronics and packaging within a 3D structure. The presented configuration will record LFPs from 8 electrodes with a 825 Hz bandwidth and an input referred noise figure of 1.77μVrms. The resulting electronics has a core area of 2.1 mm2 and a power budget of 92 μW

This paper presents an optical communication system, implementing a UWB-inspired pulsed coding technique, for emerging high throughput bio-applications such as brain machine interfaces. The proposed solution employs sub-nanosecond laser pulses that, compared to the state-of-the-art, allows for high bit rate transmissions and reduced power consumption. The overall architecture consist of a transmitter and receiver that employ a pulsed semiconductor laser and a small sensitive area photodiode. This can allow for CMOS integration into a compact silicon footprint (estimated lower than 1 mm2 in a 0.18 μm technology). The analogue circuits presented herein have been implemented using discrete off-the-shelf components. These provide the bias and drive signals for laser pulse generation, photodiode signal detection and conditioning. Moreover, the digital sub-system for data coding and decoding processes have been implemented on a FPGA board through VHDL description language. Experimental results validate the overall functionality of the proposed system using a diffuser between transmitter and receiver to emulate skin/tissue. This shows the capability of operating at bit rates up to 250 Mbps achieving BER less than 10−9 and power efficiency as low as 24pJ/bit. These results enable, for example, the transmission of a 1000-channel neural recording system sampled at 16kHz with 16-bit resolution.

A closed-loop optogenetic system for curing epilepsy is presented in this work. As it shown at figure 1, the system consists of a cortical brain implant with LEDs and recording electrodes, a customer designed CMOS chip[1][2][3] and a controller. The brain activities are recorded by the implant with recording electronics in a CMOS chip, the signals are processed by the controller, and the results are send back to the CMOS chip for delivering LED stimulation commands.

Next generation brain machine interfaces are targeting millimeter-scale implants that are freely floating and completely wireless. It is essential these systems achieve good power transmission efficiency but are also compatible with microsystem technologies. This paper presents two schemes for implementing mm-scale coils for power delivery by electromagnetic coupling — on-chip and wire-wound. A set of on-chip coils have been fabricated using a 0.35 μm CMOS technology with thick top metal option (3 μm aluminium). These achieve a maximum Q-factor of 16.37. The second approach describes wire-wound coils that have been fabricated using bondwire (25 μm gold), achieving a Q-factor of 24.54. This work develops the relevant analytical models, equivalent simulation models, and reports results using both finite element modeling (simulation) and experimental measurement of the fabricated devices. Finally, we compare results and discuss the relative merits of each approach.

Emerging applications for implantable devices are requiring multi-unit systems with intrabody transmission of power and data through wireline interfaces. This paper proposes a novel method for power delivery within such a configuration that makes use of closed loop dynamic regulation. This is implemented for an implantable application requiring a single master and multiple identical slave devices utilising a parallel-connected 4-wire interface. The power regulation is achieved within the master unit through closed loop monitoring of the current consumption to the wired link. Simultaneous power transfer and full-duplex data communication is achieved by superimposing the power carrier and downlink data over two wires and uplink data over a second pair of wires. Measured results using a fully isolated (AC coupled) 4-wire lead, demonstrate this implementation can transmit up to 120 mW of power at 6 V (at the slave device, after eliminating any losses). The master device has a maximum efficiency of 80 % including a dominant dynamic power loss. A 6 V constant supply at the slave device is recovered 1.5 ms after a step of 22 mA.

This paper proposes a novel method for integrating CMOS microelectronics with microwire-based electrodes for next generation implantable brain machine interfaces. There is strong evidence to suggest that microwire-based electrodes outperform micromachined and polymer-based electrodes in terms of signal integrity and chronic viability. Furthermore, it has been shown that the recording of Local Field Potentials (LFPs) is more robust to tissue damage and scar tissue growth when compared to action potentials. This work therefore investigates the suitability of microwire electrodes for LFP recording by studying the electrical properties of key materials. We identify Niobium (Nb) as a candidate material with highly desirable properties. There is however also an inherent incompatibility when it comes to connection of microwire-based electrodes to silicon chips. Here we present a new process flow utilising a recessed glass substrate for mechanical support, silicon interposer for interconnection, and electroplating for contact adhesion. Furthermore, the proposed structure lends itself to hermetic encapsulation towards gas cavity based micropackages.

This work presents a computationally efficient real-time adaptive clustering algorithm that recognizes and adapts to dynamic changes observed in neural recordings. The algorithm consists of an off-line training phase that determines initial cluster positions and an on-line operation phase that continuously tracks drifts in clusters and periodically verifies acute changes in cluster composition. Analysis of chronic recordings from non-human primates shows that adaptive clustering achieves an improvement of 14% in classification accuracy and demonstrates an ability to recognize acute changes with 78% accuracy, with significantly improved computational efficiency compared to the state-of-the-art. The presented algorithm is suitable for long-term chronic monitoring of neural activity in many applications of neuroscience research and control of neural prosthetics and assistive devices.

This paper presents a hardware realisation of a novel ECG baseline drift removal that preserves the ECG signal integrity. The microcontroller implementation detects the fiducial markers of the ECG signal and the baseline wander estimation is achieved through a weighted piecewise linear interpolation. This estimated drift is then removed to recover a “clean” ECG signal without significantly distorting the ST segment. Experimental results using real data from the MIT-BIH Arrhythmia Database (recording 100 and 101) with added baseline wander (BWM1) from the MIT-BIH Noise Stress Database show an average root mean square error of 34.3 μV (mean), 30.4 μV (median) and 18.4 μV (standard deviation) per heart beat.

This paper presents a 64-bit on-chip identification system featuring low power consumption and randomness compensation for multi-node bio-implantable devices. A sense amplifier based bit-cell is proposed to realize the silicon physical unclonable function, providing a unique value whose probability has a uniform distribution and minimized influence from the temperature and supply variation. The entire system is designed and implemented in a typical 0.35 μm CMOS technology, including an array of 64 bit-cells, readout circuits, and digital controllers for data interfaces. Simulated results show that the proposed bit-cell design achieved a uniformity of 50.24% and a uniqueness of 50.03% for generated IDs. The system achieved an energy consumption of 6.0 pJ per bit with parallel outputs and 17.3 pJ per bit with serial outputs.

The paper presents a novel topology of a continuous-time analogue-to-digital converter (CT-ADC) featuring ultra-low static power consumption, activity-dependent dynamic consumption, and a compact footprint. This is achieved by utilising a novel charge-packet based threshold generation method, that alleviates the requirement for a conventional feedback DAC. The circuit has a static power consumption of 3.75 μW, with dynamic energy of 1.39pJ/conversion level. This type of converter is thus particularly well-suited for biosignals that are generally sparse in nature. The circuit has been optimised for neural spike recording by capturing a 3 kHz bandwidth with 8-bit resolution. For a typical extracellular neural recording the average power consumption is in the order of ∼4 μW. The circuit has been implemented in a commercially available 0.35 μm CMOS technology with core occupying a footprint of 0.12 mm2.

This paper presents a multichannel, ultra-low-noise arbitrary signal generation platform for emulating a wide range of different biopotential signals (e.g. ECG, EEG, etc). This is intended for use in the test, measurement and demonstration of bioinstrumentation and medical devices that interface to electrode inputs. The system is organized in 3 key blocks for generating, processing and converting the digital data into a parallel high performance analogue output. These blocks consist of: (1) a Raspberry Pi 3 (RPi3) board; (2) a custom Field Programmable Gate Array (FPGA) board with low-power IGLOO® Nano device; and (3) analogue board including the Digital-to-Analogue Converters (DACs) and output circuits. By implementing the system this way, good isolation can be achieved between the different power and signal domains. This mixed-signal architecture takes in a high bitrate SDIO (Secure Digital Input Output) stream, recodes and packetizes this to drive two multichannel DACs, with parallel analogue outputs that are then attenuated and filtered. The system achieves 32-parallel output channels each sampled at 48kS/s, with a 10 kHz bandwidth, 110 dB dynamic range and μV-level output noise.

This paper describes a novel system for recovering power and providing full-duplex communication over an AC-coupled 4-wire lead between active implantable devices. The target application requires a single Chest Device be connected to a Brain Implant consisting of multiple identical optrodes that record neural activity and provide closed loop optical stimulation. The interface is integrated within each optrode SoC allowing full-duplex and fully-differential communication based on Manchester encoding. The system features a head-to-chest uplink data rate (1.6 Mbps) that is higher than that of the chest-to-head downlink (100kbps) superimposed on a power carrier. On-chip power management provides an unregulated 5 V DC supply with up to 2.5 mA output current for stimulation, and a regulated 3.3 V with 60 dB PSRR for recording and logic circuits. The circuit has been implemented in a 0.35 μm CMOS technology, occupying 1.4 mm 2 silicon area, and requiring a 62.2 μA average current consumption.

This paper presents a novel oscillator based filter structure for processing time-domain signals with linear dynamics that extensively uses digital logic by construction. Such a mixed signal topology is a key component for allowing efficient processing of asynchronous time encoded signals that does not necessitate external clocking. A miniaturized primitive is introduced as analogue time-domain memory that can be modelled, synthesized, and incorporated in closed loop mixed signal accelerators to realize more complex linear or non-linear computational systems. This is contextualized by demonstrating a compact low power filter operating at 0.45 V in 65 nm CMOS. Simulation results are presented showing an excess of 50 dB dynamic range with a FOM of 7fJ/pole which promises an order of magnitude improvement on state-of-the-art filters in nanometre CMOS.

This paper proposes a self-diagnostic subsystem for a new generation of brain implants with active electronics. The primary objective of such probes is to deliver optical pulses to optogenetic tissue and record the subsequent activity, but lifetime is currently unknown. Our proposed circuits aim to increase the safety of implanting active electronic probes into human brain tissue. Therefore, prolonging the lifetime of the implant and reducing the risks to the patient. The self-diagnostic circuit will examine the optical emitter against any abnormality or malfunctioning. The fracture sensor examines the optrode against any rapture or insertion breakage. The optrode including our diagnostic subsystem and fracture sensor has been designed and successfully simulated at 350nm AMS technology node and sent for manufacture.