Publications
274 results found
Rapeaux A, 2020, Enhancing Selectivity of Minimally Invasive Peripheral Nerve Interfaces using Combined Stimulation and High Frequency Block: from Design to Application
The discovery of the excitable property of nerves was a fundamental step forward in our knowledge of the nervous system and our ability to interact with it. As the injection of charge into tissue can drive its artificial activation, devices have been conceived that can serve healthcare by substituting the input or output of the peripheral nervous system when damage or disease has rendered it inaccessible or its action pathological. Applications are far-ranging and transformational as can be attested by the success of neuroprosthetics such as the cochlear implant. However, the body's immune response to invasive implants have prevented the use of more selective interfaces, leading to therapy side-effects and off-target activation. The inherent tradeoff between the selectivity and invasiveness of neural interfaces, and the consequences thereof, is still a defining problem for the field. More recently, continued research into how nervous tissue responds to stimulation has led to the discovery of High Frequency Alternating Current (HFAC) block as a stimulation method with inhibitory effects for nerve conduction. While leveraging the structure of the peripheral nervous system, this neuromodulation technique could be a key component in efforts to improve the selectivity-invasiveness tradeoff and provide more effective neuroprosthetic therapy while retaining the safety and reliability of minimally invasive neural interfaces. This thesis describes work investigating the use of HFAC block to improve the selectivity of peripheral nerve interfaces, towards applications such as bladder control or vagus nerve stimulation where selective peripheral nerve interfaces cannot be used, and yet there is an unmet need for more selectivity from stimulation-based therapy. An overview of the underlyingneuroanatomy and electrophysiology of the peripheral nervous system combined with a review of existing electrode interfaces and electrochemistry will serve to inform the problem space. Origina
Luo J, Firflionis D, Turnball M, et al., 2020, The neural engine: a reprogrammable low power platform for closed-loop optogenetics, IEEE Transactions on Biomedical Engineering, Vol: 67, Pages: 3004-3015, ISSN: 0018-9294
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 developed a low power neural engine with an optimized set of algorithms which can operate under a power cycling domain. By integrating with custom designed brain implant chip, we have demonstrated the operational applicability to the closed-loop modulating neural activities in in-vitro 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 long-term recording performance. The overall system consumes only 2.93mA during operation with a biological recording frequency 50Hz 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.
Williams I, Brunton E, Rapeaux A, et al., 2020, SenseBack-an implantable system for bidirectional neural interfacing, IEEE Transactions on Biomedical Circuits and Systems, Vol: 14, Pages: 1079-1087, ISSN: 1932-4545
Chronic in-vivo neurophysiology experiments require highly miniaturized, remotely powered multi-channel neural interfaces which are currently lacking in power or flexibility post implantation. In this article, to resolve this problem we present the SenseBack system, a post-implantation reprogrammable wireless 32-channel bidirectional neural interfacing that can enable chronic peripheral electrophysiology experiments in freely behaving small animals. The large number of channels for a peripheral neural interface, coupled with fully implantable hardware and complete software flexibility enable complex in-vivo studies where the system can adapt to evolving study needs as they arise. In complementary ex-vivo and in-vivo preparations, we demonstrate that this system can record neural signals and perform high-voltage, bipolar stimulation on any channel. In addition, we demonstrate transcutaneous power delivery and Bluetooth 5 data communication with a PC. The SenseBack system is capable of stimulation on any channel with ±20 V of compliance and up to 315 μA of current, and highly configurable recording with per-channel adjustable gain and filtering with 8 sets of 10-bit ADCs to sample data at 20 kHz for each channel. To the best of our knowledge this is the first such implantable research platform offering this level of performance and flexibility post-implantation (including complete reprogramming even after encapsulation) for small animal electrophysiology. Here we present initial acute trials, demonstrations and progress towards a system that we expect to enable a wide range of electrophysiology experiments in freely behaving animals.
Zamora M, Toth R, Morgante F, et al., 2020, DyNeuMo Mk-1: Design and Pilot Validation of an Investigational Motion-Adaptive Neurostimulator with Integrated Chronotherapy
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 ‘DyNeuMo Mk-1’, 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 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 shocks for urinary incontinence. With appropriate design controls in place, first-in-human research trials are now being prepared to explore the utility of automated motion-adaptive algorithms.
Rapeaux A, Constandinou TG, 2020, An HFAC block-capable and module-extendable 4-channel stimulator for acute neurophysiology, Journal of Neural Engineering, Vol: 17, ISSN: 1741-2552
Objective. This paper describes the design, testing and use of a novel multichannel block-capable stimulator for acute neurophysiology experiments to study highly selective neural interfacing techniques. This paper demonstrates the stimulator's ability to excite and inhibit nerve activity in the rat sciatic nerve model concurrently using monophasic and biphasic nerve stimulation as well as high-frequency alternating current (HFAC). Approach. The proposed stimulator uses a Howland Current Pump circuit as the main analogue stimulator element. 4 current output channels with a common return path were implemented on printed circuit board using Commercial Off-The-Shelf components. Programmable operation is carried out by an ARM Cortex-M4 Microcontroller on the Freescale freedom development platform (K64F). Main results. This stimulator design achieves ± 10 mA of output current with ± 15 V of compliance and less than 6 µA of resolution using a quad-channel 12-bit external DAC, for four independently driven channels. This allows the stimulator to carry out both excitatory and inhibitory (HFAC block) stimulation. DC Output impedance is above 1 M Ω. Overall cost for materials i.e. PCB boards and electronic components is less than USD 450 or GBP 350 and device size is approximately 9 cm × 6 cm × 5 cm. Significance. Experimental neurophysiology often requires significant investment in bulky equipment for specific stimulation requirements, especially when using HFAC block. Different stimulators have limited means of communicating with each other, making protocols more complicated. This device provides an effective solution for multi-channel stimulation and block of nerves, enabling studies on selective neural interfacing in acute scenarios with an affordable, portable and space-saving design for the laboratory. The stimulator can be further upgraded with additional modules to extend functionality while maintaining straightforward programming
Savolainen OW, Constandinou TG, 2020, Lossless compression of intracortical extracellular neural recordings using non-adaptive huffman encoding, 42nd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 4318-4321, ISSN: 1557-170X
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.
Savolainen OW, Constandinou TG, 2020, Predicting single-unit activity from local field potentials with LSTMs, 42nd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 884-887, ISSN: 1557-170X
This paper investigates to what extent Long ShortTerm 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 40-50 Hz, 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.
Tossell K, Yu X, Soto BA, et al., 2020, Sleep deprivation triggers somatostatin neurons in prefrontal cortex to initiate nesting and sleep via the preoptic and lateral hypothalamus, Publisher: bioRxiv
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.
Liu Y, Urso A, Martins da Ponte R, et al., 2020, Bidirectional bioelectronic interfaces: system design and circuit implications, IEEE Solid-State Circuits Magazine, Vol: 12, Pages: 30-46, ISSN: 1943-0582
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.
De Marcellis A, Di Patrizio Stanchieri G, Faccio M, et al., 2020, A 300 Mbps 37 pJ/bit pulsed optical biotelemetry, IEEE Transactions on Biomedical Circuits and Systems, Vol: 14, Pages: 441-451, ISSN: 1932-4545
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 −10 , and an energy efficiency of 37 pJ/bit. This can communicate, for example, 1,024 channels of broadband neural data sampled at 18 kHz, 16-bit with only 11 mW power consumption.
Ahmadi N, Constandinou TG, Bouganis C-S, 2020, Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning
<jats:p>Robustness and decoding accuracy remain major challenges in the clinical translation of intracortical brain-machine interface (BMI) systems. In this work, we show that a signal/decoder co-design methodology (exploiting the synergism between the input signal and decoding algorithm within the design development process) can be used to yield robust and accurate BMI decoding performance. Specifically, through applying this process, we propose the combination of using entire spiking activity (ESA) as the input signal and quasi-recurrent neural network (QRNN) based deep learning as the decoding algorithm. We evaluated the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of a non-human primate. Our proposed method yielded consistently higher decoding performance than any other methods previously reported across long-term recording sessions. Its high decoding performance could sustain, even when spikes were removed from the raw signals. Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs.</jats:p>
Ahmadi N, Constandinou TG, Bouganis C-S, 2020, Impact of referencing scheme on decoding performance of LFP-based brain-machine interface
<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>There has recently been an increasing interest in local field potential (LFP) for brain-machine interface (BMI) applications due to its desirable properties (signal stability and low bandwidth). LFP is typically recorded with respect to a single unipolar reference which is susceptible to common noise. Several referencing schemes have been proposed to eliminate the common noise, such as bipolar reference, current source density (CSD), and common average reference (CAR). However, to date, there have not been any studies to investigate the impact of these referencing schemes on decoding performance of LFP-based BMIs.</jats:p></jats:sec><jats:sec><jats:title>Approach</jats:title><jats:p>To address this issue, we comprehensively examined the impact of different referencing schemes and LFP features on the performance of hand kinematic decoding using a deep learning method. We used LFPs chronically recorded from the motor cortex area of a monkey while performing reaching tasks.</jats:p></jats:sec><jats:sec><jats:title>Main results</jats:title><jats:p>Experimental results revealed that local motor potential (LMP) emerged as the most informative feature regardless of the referencing schemes. Using LMP as the feature, CAR was found to yield consistently better decoding performance than other referencing schemes over long-term recording sessions.</jats:p></jats:sec><jats:sec><jats:title>Significance</jats:title><jats:p>Overall, our results suggest the potential use of LMP coupled with CAR for enhancing the decoding performance of LFP-based BMIs.</jats:p></jats:sec>
Ahmadi N, Constandinou TG, Bouganis C-S, 2020, Inferring entire spiking activity from local field potentials with deep learning
<jats:title>ABSTRACT</jats:title><jats:p>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.</jats:p>
Wang G, Constandinou TG, Tang K-T, 2020, Editorial, IEEE Transactions on Biomedical Circuits and Systems, Vol: 14, Pages: 1-1, ISSN: 1932-4545
Rapeaux A, Constandinou T, 2020, A block-capable and module-extendable 4-channel stimulator for acute neurophysiology, Publisher: bioRxiv
Objective: This paper describes the design, testing and use of a novel multichannel block-capable stimulator for acute neurophysiology experiments to study highly selective neural interfacing techniques. This paper demonstrates the stimulator's ability to excite and inhibit nerve activity in the rat sciatic nerve model concurrently using monophasic and biphasic nerve stimulation as well as high-frequency alternating current (HFAC). Approach: The proposed stimulator uses a Howland Current Pump circuit as the main analogue stimulator element. 4 current output channels with a common return path were implemented on printed circuit board using Commercial Off-The-Shelf components. Programmable operation is carried out by an ARM Cortex-M4 Microcontroller on the Freescale freedom development platform (K64F). Main Results: This stimulator design achieves +-10 mA of output current with +-15V of compliance and less than 6 uA of resolution using a quad-channel 12-bit external DAC, for four independently driven channels. This allows the stimulator to carry out both excitatory and inhibitory (HFAC block) stimulation. DC Output impedance is above 1 Mohm. Overall cost is less than USD 450 or GBP 350 and device size is approximately 9 cm x 6 cm x 5 cm. Significance: Experimental neurophysiology often requires significant investment in bulky equipment for specific stimulation requirements, especially when using HFAC block. Different stimulators have limited means of communicating with each other, making protocols more complicated. This device provides an effective solution for multi-channel stimulation and block of nerves, enabling studies on selective neural interfacing in acute scenarios with an affordable, portable and space-saving design for the laboratory. The stimulator can be further upgraded with additional modules to extend functionality while maintaining straightforward programming and integration of functions with one controller.
Haci D, Liu Y, Ghoreishizadeh SS, et al., 2020, Key Considerations for Power Management in Active Implantable Medical Devices, 11th Latin American Symposium on Circuits & Systems (LASCAS), Publisher: IEEE, ISSN: 2330-9954
Delbruck T, Elfadel IAM, Muzaffar S, et al., 2020, Confession Session: Lessons Learned the Hard Way, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
De Marcellis A, Stanchieri GDP, Faccio M, et al., 2020, Fast-Response Paradigm of Si Photodiode Array to Increase the Effective Sensitive Area of Detectors in Wireless Optical Biotelemetry Links, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
Schiavone PD, Rossi D, Liu Y, et al., 2020, Neuro-PULP: A Paradigm Shift Towards Fully Programmable Platforms for Neural Interfaces, 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Publisher: IEEE, Pages: 50-54
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De Marcellis A, Stanchieri GDP, Palange E, et al., 2019, A 0.35μm CMOS UWB-inspired bidirectional communication system-on-chip for transcutaneous optical biotelemetry links, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 1-4
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 off-chip pulsed vertical cavity semiconductor lasers by means of a digital data coding subsystem and all the needed bias and driving circuits. On the other hand, the receiver manages 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 entire solution designed at transistor level has been fabricated in AMS 0.35µm standard CMOS technology into a compact silicon footprint lower than 0.13mm2 employing only 113 transistors and 1 resistor. A specific PCB has been developed together with a suitable test bench implemented on Xilinx Virtex-6 XC6VLX240T FPGA board to properly evaluate the performances and the main characteristics of the ASIC. Furthermore, a 6 GHz, 20 GS/s LeCroy WaveMaster 8600A digital oscilloscope has been employed to investigate the system time response. Preliminary experimental results validated the correct functionality of the overall integrated system demonstrating also its capability to operate, also in a bidirectional mode, at bit rates up to 250 Mbps with pulse widths up to 1.2ns and a minimum total power efficiency of about 160 pJ/bit in the conditions for which the transmitter and the receiver work simultaneously onto the same chip. These results make the developed solution suitable for high performances bidirectional optical biotelemetry links to be applied, e.g., to implantable neural recording/stimulation transcutaneous platforms that generally require communication
Cavuto M, Hallam R, Rapeaux A, et al., 2019, Live demonstration: a public engagement platform for invasive neural interfaces, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 1-1
Neural interfaces, and more specifically ones ofthe invasive/implantable variety, today are a topic of muchcontroversy, often making the general public uncomfortable andintimidated. We have thus devised a bespoke interactive demoto help people understand brain implants and their need inthe age of wearable devices, with the secondary objective ofintroducing the wireless cortical neural probe that we, at NGNI(Next Generation Neural Interfaces) lab, are developing.
Wong S, Ekanayake J, Liu Y, et al., 2019, An impedance probing system for real-time intra-operative brain tumour tissue discrimination, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 1-4
The ability to perform realtime diagnostics of tissueintraoperatively can greatly enhance the precision and effective-ness of the underlying surgery, for example, in tumour resection.To achieve this however would require a miniature tool ableto performin situ, in-vivocharacterisation for distinguishingbetween different types of tissues. In this work, we exploredthe feasibility and requirements of implementing a portableimpedance characterisation system for brain tumour detection.We proposed and implemented a novel system based on PCB-based instrumentation using a square four-electrode microendo-scopic probe. The system uses a digital-to-analogue converterto generate a multi-tone sinusoid waveform, and a floating bi-directional voltage-to-current converter to output the differentialstimulation current to one pair of electrodes. The other pairof electrodes are connected to the sensing circuit based on aninstrumentation amplifier. The recorded data is pre-processed bythe micro-controller and then analysed on a host computer. Toevaluate the system, tetrapolar impedances have been recordedfrom a number of different electrode configurations to sense pre-defined resistance values. The overall system consumes 143 mAcurrent, achieve 0.1% linearity and 15μV noise level, with amaximum signal bandwidth of 100 kHz. Initial experimentalresults on tissue were carried out on a piece of rib-eye steak.Electrical impedance maps (EIM) and contour plots were thenreconstructed to represent the impedance value in different tissue region.
Williams I, Rapeaux A, Pearson J, et al., 2019, SenseBack - implant considerations for an implantable neural stimulation and recording device, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 1-4
This paper describes a fully implantable and highlycompact neural interface platform for chronic (>6 month) ratand small rodent experiments. It provides 32 channels of highlyflexible neural stimulation and recording with wireless controland data readout, as well as wireless transcutaneous power. Allthe system firmware is fully upgradeable over the air (even afterimplantation) allowing future enhancements such as closed loopoperation or data filtering. This paper focuses on the implantconsiderations – i.e. design and manufacture of the physicalplatform, encapsulation, wireless connections and testing.
Hsieh B, Harding E, Wisden W, et al., 2019, A miniature neural recording device to investigate sleep and temperature regulation in mice, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 1-4
Sleep is an important and ubiquitous process that,despite decades of research, a large part of its underlyingbiological circuity still remain elusive. To conduct research inthis field, many devices capable of recording neural signalssuch as LFP and EEG have been developed. However, most ofthese devices are unsuitable for sleep studies in mice, the mostcommonly used animals, due to their size and weight. Thus, thispaper presents a novel 4 channel, compact ( 2.1cm by 1.7cm )and lightweight ( 3.6g ) neural-logging device that can recordfor 3 days on just two 0.6g zinc air 312 batteries. Instead ofthe typical solution of using multiple platforms, the presenteddevice integrates high resolution EEG, EMG and temperaturerecordings into one platform. The onboard BLE module allowsthe device to be controlled wirelessly as well as stream data in realtime, enabling researchers to check the progress of the recordingwith minimal animal disturbance. The device demonstrates itsability to accurately record EEG and temperature data throughthe long 24 hour in-vivo recordings conducted. The obtainedEEG data could be easily sleep scored and the temperaturesvalues were all within expected physiological range.
Haci D, Mifsud A, Liu Y, et al., 2019, In-body wireline interfacing platform for multi-module implantable microsystems, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 1-4
The recent evolution of implantable medical devicesfrom single-unit stimulators to modern implantable microsys-tems, has driven the need for distributed technologies, in whichboth the implant system and functions are partitioned across mul-tiple active devices. This multi-module approach is made possiblethanks to novel network architectures, allowing for in-body powerand data communications to be performed using implantableleads. This paper discusses the challenges in implementing suchinterfacing system and presents a platform based on one centralimplant (CI) and multiple peripheral implants (PIs) using a cus-tom 4WiCS communication protocol. This is implemented in PCBtechnology and tested to demonstrate intrabody communicationcapabilities and power transfer within the network. Measuredresults show CI-to-PI power delivery achieves 70%efficiency inexpected load condition, while establishing full-duplex data linkwith up to 4 PIs simultaneously.
Feng P, Maslik M, Constandinou T, 2019, EM-lens enhanced power transfer and multi-node data transmission for implantable medical devices, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 1-4
This paper presents a robust EM-lens-enhancedwireless power transmission system and a novel multiple-nodedata communication method for distributed implantable medicaldevices. The proposed techniques can solve the common issuescaused by multiple implanted devices, such as low power transferefficiency through biological tissues, uneven delivered powerfor distributed devices and interference between simultaneouswireless power and data transmission. A prototype system hasbeen manufactured with discrete components on FR4 substrateas a proof of concept. The EM-Lens-enhanced inductive linkscan expand the power coverage of transmitting (Tx) coil from9 mm×5 mm to 14 mm×13 mm, and double the recovered DCvoltage from 1.8 V to 3.2 V at 12.5 mm distance. Data commu-nication is achieved by novel low-power back-scattering CDMAscheme. This permits transmission of data from several nodesall operating with the same carrier frequency simultaneouslyreflecting the power carriers to the primary side. In this paper,we demonstrate simultaneous communication between two nodesat 125 kbps with 1.05 mW power consumption.
Liu Y, Constandinou TG, Georgiou P, 2019, Ultrafast large-scale chemical sensing with CMOS ISFETs: a level-crossing time-domain approach, IEEE Transactions on Biomedical Circuits and Systems, Vol: 13, Pages: 1201-1213, ISSN: 1932-4545
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 0.18um CMOS technology, with a pixel size of 26μm×26μm and a TDC area of 26μm×180μm. Measured results demonstrate the system is able to sense reliably with an average pH sensitivity of 30mVpH, whilst being able to compensate for sensor offset by up to ±7V. A resolution of 0.013pH is achieved and noise measurements show an integrated noise of 0.08pH within 2-500Hz and SFDR of 42.6dB. Total power consumption is 11.286mW.
Chew DJ, Constandinou TG, Gupta I, et al., 2019, Bioelectronic medicines: past, present and future. Highlights from The Society for Medicines Research Symposium, Drugs of the Future, Vol: 44, Pages: 895-902, ISSN: 0377-8282
On October 1, 2019, the Society for Medicines Research (SMR) held its first symposium on "Bioelectronic medicines, past, present and future" at the Royal Academy of Engineering in London. The meeting was attended by 145 participants and was supported by Galvani Bioelectronics, IEEE-CAS Society, IEEE-Brain Initiative, BIOS, Heraeus, CorTec and the IT'IS Foundation.
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.
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