Publications
276 results found
De Marcellis A, Stanchieri GDP, Palange E, et al., 2019, A 0.35 mu m CMOS UWB-inspired bidirectional communication system-on-chip for transcutaneous optical biotelemetry links, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, ISSN: 2163-4025
In this paper we report on the fabrication, implementation and experimental characterization of an integrated bidirectional communication System-on-Chip (SoC) for transcutaneous bidirectional optical biotelemetry links. The proposed architecture implements a UWB-inspired pulsed coding technique and contains a transmitter and a receiver to achieve a simultaneous bidirectional link. The transmitter generates sub- nanosecond current pulses to directly drive offchip pulsed vertical cavity semiconductor lasers by means of a digital data coding subsystem and all the needed bias and driving circuits. The receiver interfaces to off-chip fast Si photodiodes and includes signal conditioning, detection and digital data decoding circuits to support high bit rate and energy efficient communication links. The SoC has been implemented in a commercially-available 0.35 mu m CMOS technology provided by AMS, occupying a compact silicon footprint of less than 0.13 mm2 employing 113 transistors and 1 resistor. This is evaluated using a testbench consisting of a custom PCB and a Xilinx Virtex-6 XC6VLX240T FPGA board. Preliminary experimental results validated the correct functionality of the overall integrated system demonstrating its capability to operate, also in a bidirectional mode, at bit rates up to 250 Mbps with pulse widths down to 1.2 ns and a minimum total power efficiency of about 160 pJ/bit in the conditions for which the transmitter and the receiver work simultaneously on the same chip. This demonstrated performance makes the optical biotelemetry particularly suitable for highly scalable (i.e., high bitrate, low energy per bit) implantable devices such as brain machine interfaces.
Han Y, Lauteslager T, Lande TS, et al., 2019, UWB radar for non-contact heart rate variability monitoring and mental state classification., Annual Meeting of the IEEE Engineering in Medicine and Biology Society, Pages: 6578-6582, ISSN: 1557-170X
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.
Lauteslager T, Tommer M, Lande TS, et al., 2019, Coherent UWB radar-on-chip for in-body measurement of cardiovascular dynamics, IEEE Transactions on Biomedical Circuits and Systems, Vol: 13, Pages: 814-824, ISSN: 1932-4545
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.
Ahmadi N, Constandinou TG, Bouganis C-S, 2019, End-to-End Hand Kinematic Decoding from LFPs Using Temporal Convolutional Network, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 1-4, ISSN: 2163-4025
In recent years, local field potentials (LFPs) haveemerged as a promising alternative input signal for brain-machine interfaces (BMIs). Several studies have demonstratedthat LFP-based BMIs could provide long-term recording stabilityand comparable decoding performance to their spike counter-parts. Despite the compelling results, however, most LFP-basedBMIs still make use of hand-crafted features which can betime-consuming and suboptimal. In this paper, we propose anend-to-end system approach based on temporal convolutionalnetwork (TCN) to automatically extract features and decodekinematics of hand movements directly from raw LFP signals.We benchmark its decoding performance against traditionalapproach incorporating long short-term memory (LSTM) de-coders driven by hand-crafted LFP features. Experimental re-sults demonstrate significant performance improvement of theproposed approach compared to the traditional approach. Thissuggests the suitability of TCN-based end-to-end system and itspotential for providng stable and high decoding performanceLFP-based BMIs.
Liu Y, Constandinou TG, Georgiou P, 2019, A 32 x 32 ISFET array with in-pixel digitisation and column-wise TDC for ultra-fast chemical sensing, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
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.
Leene LB, Letchumanan S, Constandinou TG, 2019, A 68 mu W 31 kS/s Fully-Capacitive Noise-Shaping SAR ADC with 102 dB SNDR, Publisher: IEEE
This paper presents a 17 bit analogue-to-digital converter that incorporatesmismatch and quantisation noise-shaping techniques into an energy-saving 10 bitsuccessive approximation quantiser to increase the dynamic range by another 42dB. We propose a novel fully-capacitive topology which allows for high-speedasynchronous conversion together with a background calibration scheme to reducethe oversampling requirement by 10x compared to prior-art. A 0.18 um CMOStechnology is used to demonstrate preliminary simulation results together withanalytic measures that optimise parameter and topology selection. The proposedsystem is able to achieve a FoMS of 183 dB for a maximum signal bandwidth of15.6 kHz while dissipating 68 uW from a 1.8 V supply. A peak SNDR of 102 dB isdemonstrated for this rate with a 0.201 mm^2 area requirement.
Ahmadi N, Cavuto ML, Feng P, et al., 2019, Towards a distributed, chronically-implantable neural interface, 9th IEEE/EMBS International Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 719-724, ISSN: 1948-3546
We present a platform technology encompassing a family of innovations that together aim to tackle key challenges with existing implantable brain machine interfaces. The ENGINI (Empowering Next Generation Implantable Neural Interfaces) platform utilizes a 3-tier network (external processor, cranial transponder, intracortical probes) to inductively couple power to, and communicate data from, a distributed array of freely-floating mm-scale probes. Novel features integrated into each probe include: (1) an array of niobium microwires for observing local field potentials (LFPs) along the cortical column; (2) ultra-low power instrumentation for signal acquisition and data reduction; (3) an autonomous, self-calibrating wireless transceiver for receiving power and transmitting data; and (4) a hermetically-sealed micropackage suitable for chronic use. We are additionally engineering a surgical tool, to facilitate manual and robot-assisted insertion, within a streamlined neurosurgical workflow. Ongoing work is focused on system integration and preclinical testing.
Cavuto ML, Constandinou TG, 2019, Investigation of insertion method to achieve chronic recording stability of a semi-rigid implantable neural probe, 9th IEEE/EMBS International Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 665-669, ISSN: 1948-3546
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.
Leene LB, Constandinou TG, 2019, A 3rd order time domain delta sigma modulator with extended-phase detection, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
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.
Troiani F, Nikolic K, Constandinou TG, 2019, Correction: Simulating optical coherence tomography for observing nerve activity: a finite difference time domain bi-dimensional model, PLoS ONE, Vol: 14, ISSN: 1932-6203
[This corrects the article DOI: 10.1371/journal.pone.0200392.].
Ahmadi N, Constandinou TG, Bouganis C-S, 2019, Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network, 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER 2019), Publisher: IEEE, Pages: 415-419
Local field potential (LFP) has gained increasing interest as an alternativeinput signal for brain-machine interfaces (BMIs) due to its informativefeatures, long-term stability, and low frequency content. However, despitethese interesting properties, LFP-based BMIs have been reported to yield lowdecoding performances compared to spike-based BMIs. In this paper, we propose anew decoder based on long short-term memory (LSTM) network which aims toimprove the decoding performance of LFP-based BMIs. We compare offline decodingperformance of the proposed LSTM decoder to a commonly used Kalman filter (KF)decoder on hand kinematics prediction tasks from multichannel LFPs. We alsobenchmark the performance of LFP-driven LSTM decoder against KF decoder drivenby two types of spike signals: single-unit activity (SUA) and multi-unitactivity (MUA). Our results show that LFP-driven LSTM decoder achievessignificantly better decoding performance than LFP-, SUA-, and MUA-driven KFdecoders. This suggests that LFPs coupled with LSTM decoder could provide highdecoding performance, robust, and low power BMIs.
Leene LB, Letchumanan S, Constandinou TG, 2019, A 68µW 31 ks/s fully-capacitive noise-shaping SAR ADC with 102 dB SNDR, ISSN: 0271-4310
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 FoMS 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.
Liu Y, Leene L, Constandinou T, 2019, An Oscillator Based Potentiostat With Switch-cap Feedback For Current Sensing Applications, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
Ahmadi N, Cavuto ML, Feng P, et al., 2019, Towards a Distributed, Chronically-Implantable Neural Interface., Publisher: IEEE, Pages: 719-724
Moly A, Luan S, Zoltan M, et al., 2018, Embedded phase-amplitude coupling based closed-loop platform for Parkinson's Disease, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 527-530, ISSN: 2163-4025
Deep Brain Stimulation (DBS) is a widely used clin-ical therapeutic modality to treat Parkinsons disease refractorysymptoms and complications of levodopa therapy. Currentlyavailable DBS systems use continuous, open-loop stimulationstrategies. It might be redundant and we could extend the batterylife otherwise. Recently, robust electrophysiological signaturesof Parkinsons disease have been characterized in motor cortexof 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. Weaim to develop a miniature, implantable and adaptive system,which only stimulates the neural target, when triggered by theoutput of the appropriate PAC algorithm. As a first step, in thispaper we compare published PAC algorithms by using humandata intra-operatively recorded from Parkinsonian patients. Wethen introduce IIR masking for later achieving fast and low-power FPGA implementation of PAC mapping for intra-operativestudies. Our closed-loop application is expected to consumesignificantly less power than current DBS systems, thereforewe can increase the battery life, without compromising clinicalbenefits.
De Marcellis A, Di Patrizio Stanchieri G, Palange E, et al., 2018, An ultra-wideband-inspired system-on-chip for an optical bidirectional transcutaneous biotelemetry, IEEE Biomedical Circuits and Systems (BioCAS) Conference 2018, Publisher: IEEE, Pages: 351-354
This paper describes an integrated communicationsystem, implementing a UWB-inspired pulsed coding technique,for an optical transcutaneous biotelemetry. The system consistsof both a transmitter and a receiver facilitating a bidirectionallink. The transmitter includes a digital data coding circuit and iscapable of generating sub-nanosecond current pulses and directlydriving an off-chip semiconductor laser diode including all biasand drive circuits. The receiver includes an integrated compactPN-junction photodiode together with signal conditioning, de-tection and digital data decoding circuits to enable a high bitrate, energy efficient communication. The proposed solution hasbeen implemented in a commercially available 0.35μm CMOStechnology provided by AMS. The circuit core occupies a compactsilicon footprint of less than 0.13 mm2(only 113 transistors and1 resistor). Post-layout simulations have validated the overallsystem operation demonstrating the ability to operate at bit ratesup to 500 Mbps with pulse widths of 300 ps with a total powerefficiency (transmitter + receiver) lower than 74 pJ/bit. Thismakes the system ideally suited for demanding applications thatrequire high bit rates at extremely low energy levels. One suchapplication is implantable brain machine interfaces requiringhigh uplink bitrates to transmit recorded data externally througha transcutaneous communication channel.
Maslik M, Lande TS, Constandinou TG, 2018, A clockless method of flicker noise suppression in continuous-time acquisition of biosignals, IEEE Biomedical Circuits and Systems (BioCAS) Conference 2018, Publisher: IEEE, Pages: 491-494
This paper presents a novel chopping method allow-ing suppression of 1/f flicker noise in continuous-time acquisitionsystems without the need for a fixed-frequency clock, stochasti-cally deriving the chopping signal from the input and henceachieving completely signal-dependent power consumption. Themethod is analysed, its basis of operation explained and a proof-of-concept implementation presented alongside simulated resultsdemonstrating an increase in achieved SNR of more than 8 dBduring acquisition of ECG, EAP and EEG signals.
Leene L, Constandinou TG, 2018, Direct digital wavelet synthesis for embedded biomedical microsystems, IEEE Biomedical Circuits and Systems (BioCAS) Conference 2018, Publisher: IEEE, Pages: 77-80
This paper presents a compact direct digital waveletsynthesizer for extracting phase and amplitude data from corticalrecordings using a feed-forward recurrent digital oscillator.These measurements are essential for accurately decoding local-field-potentials in selected frequency bands. Current systemsextensively to rely large digital cores to efficiently performFourier or wavelet transforms which is not viable for manyimplants. The proposed system dynamically controls oscillation togenerate frequency selective quadrature wavelets instead of usingmemory intensive sinusoid/cordic look-up-tables while retainingrobust digital operation. A MachXO3LF Lattice FPGA is used topresent the results for a 16 bit implementation. This configurationrequires 401 registers combined with 283 logic elements andalso accommodates real-time reconfigurability to allow ultra-low-power sensors to perform spectroscopy with high-fidelity.
Feng P, Constandinou TG, 2018, Robust wireless power transfer to multiple mm-scale freely-positioned Neural implants, IEEE Biomedical Circuits and Systems (BioCAS) Conference 2018, Publisher: IEEE, Pages: 363-366
This paper presents a novel wireless power transfer(WPT) scheme that consists of a two-tier hierarchy of near-field inductively coupled links to provide efficient power transferefficiency (PTE) and uniform energy distribution for mm-scalefree-positioned neural implants. The top tier facilitates a tran-scutaneous link from a scalp-worn (cm-scale) primary coil toa subcutaneous array of smaller, parallel-connected secondarycoils. These are then wired through the skull to a correspondingset of parallel connected primary coils in the lower tier, placedepidurally. These then inductively couple to freely positioned(mm-scale) secondary coils within each subdural implant. Thisarchitecture has three key advantages: (1) the opportunity toachieve efficient energy transfer by utilising two short-distanceinductive links; (2) good uniformity of the transdural powerdistribution through the multiple (redundant) coils; and (3) areduced risk of infection by maintaining the dura protecting theblood-brain barrier. The functionality of this approach has beenverified and optimized through HFSS simulations, to demonstratethe robustness against positional and angular misalignment. Theaverage 11.9% PTE and 26.6% power distribution deviation(PDD) for horizontally positioned Rx coil and average 2.6% PTEand 62.8% power distribution deviation for the vertical Rx coilhave been achieved.
Lauteslager T, Tommer M, Lande TS, et al., 2018, Cross-body UWB radar sensing of arterial pulse propagation and ventricular Dynamics, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 165-168
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.
Haci D, Liu Y, Ghoreishizadeh S, et al., 2018, Design considerations for ground referencing in multi-module neural implants, IEEE Biomedical Circuits and Systems (BioCAS) Conference 2018, Publisher: IEEE, Pages: 563-566
Implantable neural interfaces have evolved in thepast decades from stimulation-only devices to closed-loop record-ing and stimulation systems, allowing both for more targetedtherapeutic techniques and more advanced prosthetic implants.Emerging applications require multi-module active implantabledevices with intrabody power and data transmission. Thisdistributed approach poses a new set of challenges relatedto inter-module connectivity, functional reliability and patientsafety. This paper addresses the ground referencing challenge inactive multi-implant systems, with a particular focus on neuralrecording devices. Three different grounding schemes (passive,drive, and sense) are presented and evaluated in terms of bothrecording reliability and patient safety. Considerations on thepractical implementation of body potential referencing circuitryare finally discussed, with a detailed analysis of their impact onthe recording performance.
Haci D, Liu Y, Nikolic K, et al., 2018, Thermally controlled lab-on-PCB for biomedical applications, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 655-658
This paper reports on the implementation andcharacterisation of a thermally controlled device forin vitrobiomedical applications, based on standard Printed Circuit Board(PCB) technology. This is proposed as a low cost alternativeto 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 manufacturedto implement as many mini-hotplate arrays. 3D multiphysicssoftware simulations show the thermal response of the modelledmini-hotplate boards to electrical current stimulation, highlight-ing their versatile heating capability. A comparison with theresults obtained by the characterisation of the fabricated PCBsdemonstrates the dual temperature sensing/heating property ofthe mini-hotplate, exploitable in a larger range of temperaturewith respect to the typical operating range of LoC devices. Thethermal system is controllable by means of external off-the-shelfcircuitry designed and implemented on a single-channel controlboard prototype.
Mazza F, Liu Y, Donaldson N, et al., 2018, Integrated devices for micro-package integrity monitoring in mm-scale neural implants, IEEE Biomedical Circuits and Systems (BioCAS) Conference 2018, Publisher: IEEE, Pages: 295-298
Recent developments in the design of active im-plantable devices have achieved significant advances, for example,an increased number of recording channels, but too oftenpractical clinical applications are restricted by device longevity.It is important however to complement efforts for increased func-tionality with translational work to develop implant technologiesthat are safe and reliable to be hosted inside the human bodyover long periods of time. This paper first examines techniquescurrently used to evaluate micro-package hermeticity and keychallenges, highlighting the need for new,in situinstrumentationthat can monitor the encapsulation status over time. Two novelcircuits are then proposed to tackle the specific issue of moisturepenetration inside a sub-mm, silicon-based package. They bothshare the use of metal tracks on the different layers of the CMOSstack to measure changes in impedance caused by moisturepresent in leak cracks or diffused into the oxide layers.
Ahmadi N, Constandinou T, Bouganis C, 2018, Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS), PLoS ONE, Vol: 13, ISSN: 1932-6203
Neurons use sequences of action potentials (spikes) to convey information across neuronal networks. In neurophysiology experiments, information about external stimuli or behavioral tasks has been frequently characterized in term of neuronal firing rate. The firing rate is conventionally estimated by averaging spiking responses across multiple similar experiments (or trials). However, there exist a number of applications in neuroscience research that require firing rate to be estimated on a single trial basis. Estimating firing rate from a single trial is a challenging problem and current state-of-the-art methods do not perform well. To address this issue, we develop a new method for estimating firing rate based on a kernel smoothing technique that considers the bandwidth as a random variable with prior distribution that is adaptively updated under an empirical Bayesian framework. By carefully selecting the prior distribution together with Gaussian kernel function, an analytical expression can be achieved for the kernel bandwidth. We refer to the proposed method as Bayesian Adaptive Kernel Smoother (BAKS). We evaluate the performance of BAKS using synthetic spike train data generated by biologically plausible models: inhomogeneous Gamma (IG) and inhomogeneous inverse Gaussian (IIG). We also apply BAKS to real spike train data from non-human primate (NHP) motor and visual cortex. We benchmark the proposed method against established and previously reported methods. These include: optimized kernel smoother (OKS), variable kernel smoother (VKS), local polynomial fit (Locfit), and Bayesian adaptive regression splines (BARS). Results using both synthetic and real data demonstrate that the proposed method achieves better performance compared to competing methods. This suggests that the proposed method could be useful for understanding the encoding mechanism of neurons in cognitive-related tasks. The proposed method could also potentially improve the performance of brain-mac
Szostak KM, Constandinou TG, 2018, Hermetic packaging for implantable microsystems: effectiveness of sequentially electroplated AuSn alloy, 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE
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 environmentto 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 350 C, therefore allowing for the integration of sensors and microsystems with CMOS electronics within a single package. Issueshowever 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 design methods and process flow, share our experiences in controlling electrochemical deposition of AuSnalloy and finally discuss usability of sequential electroplating process for the formation of hermetic eutectic bonds.
Ahmadi N, Constandinou TG, Bouganis C, 2018, Spike rate estimation using Bayesian Adaptive Kernel Smoother (BAKS) and its application to brain machine interfaces, 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE
Brain Machine Interfaces (BMIs) mostly utilise spike rate as an input feature for decoding a desired motor output as it conveys a useful measure to the underlying neuronal activity. The spike rate is typically estimated by a using non-overlap binning method that yields a coarse estimate. There exist several methods that can produce a smooth estimate which could potentially improve the decoding performance. However, these methods are relatively computationally heavy for real-time BMIs. To address this issue, we propose a new method for estimating spike rate that is able to yield a smooth estimate and also amenable to real-time BMIs. The proposed method, referred to as Bayesian adaptive kernel smoother (BAKS), employs kernel smoothing technique that considers the bandwidth as a random variable with prior distribution which is adaptively updated through a Bayesian framework. With appropriate selection of prior distribution and kernel function, an analytical expression can be achieved for the kernel bandwidth. We apply BAKS and evaluate its impact on of fline BMI decoding performance using Kalman filter. The results show that overlap BAKS improved the decoding performance up to 3.33% and 12.93% compared to overlap and non-overlapbinning methods, respectively, depending on the window size. This suggests the feasibility and the potential use of BAKS method for real-time BMIs.
Rapeaux A, Brunton E, Nazarpour K, et al., 2018, Preliminary study of time to recovery of rat sciatic nerve from high frequency alternating current nerve block, 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE
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 recoveryof 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 amplitudeand 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 bemeasured 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 suggestthat 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.
Feng P, Yeon P, Cheng Y, et al., 2018, Chip-scale coils for millimeter-sized bio-implants, IEEE Transactions on Biomedical Circuits and Systems, Vol: 12, Pages: 1088-1099, ISSN: 1932-4545
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 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 experimentally measurements. Specifically, we prototype structures that are constrained to a 4mm x 4mm silicon substrate i.e. the planar in-/above-CMOS coils have outer diameter <4mm, whereas the around-CMOS coil has inner diameter of 4mm. The in-CMOS and above-CMOS coils have metal film thicknesses of 3μm aluminium and 25μm gold, respectively, whereas the around-CMOS coil is fabricated by winding a 25μm gold bonding-wire around the substrate. The measured quality factors (Q) of the mm-scale Rx coils are 10.5 @450.3MHz (in-CMOS), 24.61 @85MHz (above-CMOS), and 26.23 @283MHz (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 are
Leene L, Constandinou TG, 2018, A 0.006mm² 1.2μW analogue-to-time converter for asynchronous bio-sensors, IEEE Journal of Solid-State Circuits, Vol: 53, Pages: 2604-2613, ISSN: 0018-9200
This work presents a low-power analogue-to-time converter (ATC) for integrated bio-sensors. The proposed circuit facilitates the direct conversion of electrode biopotential 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 maximise the potential powersavings during sensor inactivity. A prototype fabricated using a 65nm CMOS technology is demonstrated with measured characteristics. Experimental results show an input-referred noise figure of 3.8μ Vrms for a 11kHz signal bandwidth while dissipating 1.2μ W from a 0.5V supply and occupying 60 ×80μ m² silicon area. This compact configuration is enabled by the proposed asynchronous readout that shapes the mismatch componentsarising from the multi-bit quantiser and the use of capacitive feedback.
Ghoreishizadeh S, Constandinou TG, 2018, On-chip Random ID Generation
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