Imperial College London

Dr Timothy Constandinou

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Reader in Neural Microsystems
 
 
 
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Contact

 

+44 (0)20 7594 0790t.constandinou Website

 
 
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Assistant

 

Miss Izabela Wojcicka-Grzesiak +44 (0)20 7594 0701

 
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Location

 

B407Bessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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202 results found

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: 1741-2560

Journal article

De Marcellis A, Di Patrizio Stanchieri G, Faccio M, Palange E, Constandinou Tet 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.

Journal article

Luo J, Firflionis D, Turnball M, Xu W, Walsh D, Escobedo-Cousin E, Soltan A, Ramezani R, Liu Y, Bailey R, O'Neill A, Donaldson N, Constandinou T, Jackson A, Degenaar Pet al., 2020, The neural engine: a reprogrammable low power platform for closed-loop optogenetics, IEEE Transactions on Biomedical Engineering, Pages: 1-10, 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.

Journal article

Wang G, Constandinou TG, Tang K-T, 2020, Editorial, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 14, Pages: 1-1, ISSN: 1932-4545

Journal article

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.

Working paper

Liu Y, Urso A, Martins da Ponte R, Costa T, Valente V, Giagka V, Serdijn WA, Constandinou TG, Denison Tet al., 2020, Bidirectional Bioelectronic Interfaces: System Design and Circuit Implications, IEEE Solid-State Circuits Magazine, Vol: 12, Pages: 30-46, ISSN: 1943-0582

Journal article

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.

Journal article

Chew DJ, Constandinou TG, Gupta I, Hann MM, Porter RA, Witherington Jet 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.

Journal article

Han Y, Lauteslager T, Lande TS, Constandinou TGet 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.

Conference paper

Lauteslager T, Tommer M, Lande TS, Constandinou TGet 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.

Journal article

Haci D, Mifsud A, Liu Y, Ghoreishizadeh S, Constandinou Tet al., 2019, In-body wireline interfacing platform for multi-module implantable microsystems, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE

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.

Conference paper

De Marcellis A, Stanchieri GDP, Palange E, Faccio M, Constandinou Tet 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

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

Conference paper

Cavuto M, Hallam R, Rapeaux A, Maslik M, Troiani F, Constandinou Tet al., 2019, Live demonstration: a public engagement platform for invasive neural interfaces, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE

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.

Conference paper

Wong S, Ekanayake J, Liu Y, Constandinou Tet al., 2019, An impedance probing system for real-time intra-operative brain tumour tissue discrimination, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE

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.

Conference paper

Williams I, Rapeaux A, Pearson J, Nazarpour K, Brunton E, Luan S, Liu Y, Constandinou Tet al., 2019, SenseBack - implant considerations for an implantable neural stimulation and recording device, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE

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.

Conference paper

Hsieh B, Harding E, Wisden W, Franks N, Constandinou Tet al., 2019, A miniature neural recording device to investigate sleep and temperature regulation in mice, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE

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.

Conference paper

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

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.

Conference paper

Ahmadi N, Bouganis C, Constandinou T, 2019, End-to-end hand kinematics decoding from local field potentials using temporal convolutional network, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE

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.

Conference paper

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, ISSN: 2163-4025

Conference paper

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.

Working paper

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.

Conference paper

Ahmadi N, Cavuto ML, Feng P, Leene LB, Maslik M, Mazza F, Savolainen O, Szostak KM, Bouganis C-S, Ekanayake J, Jackson A, Constandinou TGet 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.

Conference paper

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.

Conference paper

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.

Conference paper

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.].

Journal article

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), Pages: 1-5

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.

Conference paper

Ahmadi N, Cavuto ML, Feng P, Leene LB, Maslik M, Mazza F, Savolainen O, Szostak KM, Bouganis C-S, Ekanayake J, Jackson A, Constandinou TGet al., 2019, Towards a Distributed, Chronically-Implantable Neural Interface., Publisher: IEEE, Pages: 719-724

Conference paper

Ahmadi N, Constandinou TG, Bouganis C-S, 2019, Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network., Publisher: IEEE, Pages: 415-419

Conference paper

Mazza F, Liu Y, Donaldson N, Constandinou TGet 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.

Conference paper

Moly A, Luan S, Mari Z, Anderson WS, Salimpour Y, Constandinou TG, Grand Let al., 2018, Embedded phase-amplitude coupling based closed-loop platform forParkinson’s Disease, 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, ISSN: 2163-4025

Deep Brain Stimulation (DBS) is a widely used clinical 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 symptom improvement, and the therapeutic effects of DBS itself. We aim to develop a miniature, implantable andadaptive 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 maskingfor 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.

Conference paper

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