Publications

Journal article

Zhang Z, Constandinou T, 2021,

Adaptive spike detection and hardware optimization towards autonomous, high-channel-count BMIs

, Journal of Neuroscience Methods, Vol: 354, ISSN: 0165-0270

BackgroundThe progress in microtechnology has enabled an exponential trend in the number of neurons that can be simultaneously recorded. The data bandwidth requirement is however increasing with channel count. The vast majority of experimental work involving electrophysiology stores the raw data and then processes this offline; to detect the underlying spike events. Emerging applications however require new methods for local, real-time processing.New MethodsWe have developed an adaptive, low complexity spike detection algorithm that combines three novel components for: (1) removing the local field potentials; (2) enhancing the signal-to-noise ratio; and (3) computing an adaptive threshold. The proposed algorithm has been optimised for hardware implementation (i.e. minimising computations, translating to a fixed-point implementation), and demonstrated on low-power embedded targets.Main resultsThe algorithm has been validated on both synthetic datasets and real recordings yielding a detection sensitivity of up to 90%. The initial hardware implementation using an off-the-shelf embedded platform demonstrated a memory requirement of less than 0.1 kb ROM and 3 kb program flash, consuming an average power of 130 μW.Comparison with Existing MethodsThe method presented has the advantages over other approaches, that it allows spike events to be robustly detected in real-time from neural activity in a completely autonomous way, without the need for any calibration, and can be implemented with low hardware resources.ConclusionThe proposed method can detect spikes effectively and adaptively. It alleviates the need for re-calibration, which is critical towards achieving a viable BMI, and more so with future ‘high bandwidth’ systems’ targeting 1000s of channels.

Journal article

Ahmadi N, Constandinou TG, Bouganis C-S, 2021,

Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning

, Journal of Neural Engineering, Vol: 18, Pages: 1-23, ISSN: 1741-2552

Objective. Brain–machine interfaces (BMIs) seek to restore lost motor functions in individuals with neurological disorders by enabling them to control external devices directly with their thoughts. This work aims to improve robustness and decoding accuracy that currently become major challenges in the clinical translation of intracortical BMIs. Approach. We propose entire spiking activity (ESA)—an envelope of spiking activity that can be extracted by a simple, threshold-less, and automated technique—as the input signal. We couple ESA with deep learning-based decoding algorithm that uses quasi-recurrent neural network (QRNN) architecture. We evaluate comprehensively the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of three non-human primates performing different tasks. Main results. Our proposed method yields consistently higher decoding performance than any other combinations of the input signal and decoding algorithm previously reported across long-term recording sessions. It can sustain high decoding performance even when removing spikes from the raw signals, when using the different number of channels, and when using a smaller amount of training data. Significance. Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs.

Journal article

Cavallo FR, Mirza KB, de Mateo S, Nikolic K, Rodriguez-Manzano J, Toumazou Cet al., 2021,

Aptasensor for quantification of leptin through PCR amplification of short DNA-aptamers.

, ACS Sensors, Vol: 6, Pages: 709-715, ISSN: 2379-3694

Protein quantification is traditionally performed through enzyme-linked immunosorbent assay (ELISA), which involves long preparation times. To overcome this, new approaches use aptamers as an alternative to antibodies. In this paper, we present a new approach to quantify proteins with short DNA aptamers through polymerase chain reaction (PCR) resulting in shorter protocol times with comparatively improved limits of detection. The proposed method includes a novel way to quantify both the target protein and the corresponding short DNA-aptamers simultaneously, which also allows us to fully characterize the performance of aptasensors. Human leptin is used as a target protein to validate this technique, because it is considered an important biomarker for obesity-related studies. In our experiments, we achieved the lowest limit of detection of 100 pg/mL within less than 2 h, a limit affected by the dissociation constant of the leptin aptamer, which could be improved by selecting a more specific aptamer. Because of the simple and inexpensive approach, this technique can be employed for Lab-On-Chip implementations and for rapid "on-site" quantification of proteins.

Journal article

Ahmadi N, Constandinou T, Bouganis C-S, 2021,

Impact of referencing scheme on decoding performance of LFP-based brain-machine interface

, Journal of Neural Engineering, Vol: 18, ISSN: 1741-2552

OBJECTIVE: 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. APPROACH: 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. MAIN RESULTS: 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. Significance Overall, our results suggest the potential use of LMP coupled with CAR for enhancing the decoding performance of LFP-based BMIs.

Conference paper

Feng P, Constandinou TG, 2021,

Autonomous Wireless System for Robust and Efficient Inductive Power Transmission to Multi-Node Implants

<jats:title>Abstract</jats:title><jats:p>A number of recent and current efforts in brain machine interfaces are developing millimetre-sized wireless implants that achieve scalability in the number of recording channels by deploying a distributed ‘swarm’ of devices. This trend poses two key challenges for the wireless power transfer: (1) the system as a whole needs to provide sufficient power to all devices regardless of their position and orientation; (2) each device needs to maintain a stable supply voltage autonomously. This work proposes two novel strategies towards addressing these challenges: a scalable resonator array to enhance inductive networks; and a self-regulated power management circuit for use in each independent mm-scale wireless device. The proposed passive 2-tier resonant array is shown to achieve an 11.9% average power transfer efficiency, with ultra-low variability of 1.77% across the network.</jats:p><jats:p>The self-regulated power management unit then monitors and autonomously adjusts the supply voltage of each device to lie in the range between 1.7 V-1.9 V, providing both low-voltage and over-voltage protection.</jats:p>

Conference paper

Occhipinti E, Mirza KB, Toumazou C, 2021,

Personalised trainer recommendation based on physical activity and genetic profile

Foot-care specialists recommend shoes by analysing the patient's gait cycle and looking for any structural or functional problems. Such methods are time consuming, inaccurate and unable to identify any risk factors that may lead to development of foot-related diseases in the future. This work presents a footwear recommendation algorithm based on genetic predispositions i.e. the genetic profile associated to selected Single Nucleotide Polymorphisms (SNPs), and the individual activity level, in addition to age, body mass index (BMI) and pronation. The algorithm, built on an Artificial Neural Network (ANN), returns a personalised recommendation for four different commercially available shoe categories (Minimalist, Stability, Motion Control, Cushioned). The activity profiles are generated based on features extracted from actual users' step count data collected via the wearable device DnaBand™, which are then combined with users' physical information and genetic profile. The Gaussian Mixture Model (GMM) has been found to best identify the relevant activity profiles' clusters. 5 case studies have been selected and used to validate the ANN output.

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Conference paper

Han Z, Francesca C, Nikolic K, Mirza K, Toumazou Cet al., 2021,

Signal identification of DNA amplification curves in custom-PCR platforms

, ISSN: 0271-4310

Custom-made, point-of-care PCR platforms are a necessary tool for rapid, point-of-care diagnostics in situations such as the current Covid-19 pandemic. However, a common issue faced by them is noisy fluorescence signals, which consist of a drifting baseline or noisy sigmoidal curve. This makes automated detection difficult and requires human verification. In this paper, we have tried to use nonlinear fitting for automated classification of PCR waveforms to identify whether amplification has taken place or not. We have presented several novel signal reconstruction techniques based on nonlinear fitting which will enable better pre-processing and automated differentiation of a valid or invalid PCR amplification curve. We have also tried to perform this classification at lower PCR cycles to reduce decision times in diagnostic tests.

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Citations: 3

Conference paper

Yilmaz S, Constandinou TG, Carrara S, 2021,

Integrated Potentiostat Design for Neurotransmitter Detection in Wireless Implants

, IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), Publisher: IEEE, Pages: 848-852, ISSN: 1548-3746

Journal article

Tringali D, Haci D, Mazza F, Nikolic K, Demarchi D, Constandinou TGet al., 2021,

Eye Accommodation Sensing for Adaptive Focus Adjustment

, 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), Pages: 7460-7464, ISSN: 1557-170X

Conference paper

Zhang J, Alexandrou G, Toumazou C, Kalofonou Met al., 2021,

Automating the Design of Cancer Specific DNA Probes Using Computational Algorithms

, 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC), Publisher: IEEE, Pages: 1852-1856, ISSN: 1557-170X

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Citations: 1

Journal article

Del Bono F, Rapeaux A, Demarchi D, Constandinou TGet al., 2021,

Translating node of Ranvier currents to extraneural electrical fields: a flexible FEM modeling approach

, 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), Pages: 4268-4272, ISSN: 1557-170X

Journal article

Bannon A, Rapeaux A, Constandinou TG, 2021,

Tiresias: A low-cost networked UWB radar system for in-home monitoring of dementia patients

, 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), Pages: 7068-7072, ISSN: 1557-170X

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Citations: 1

Conference paper

Stanchieri GDP, Battisti G, De Marcellis A, Faccio M, Palange E, Constandinou TGet al., 2021,

A New Multilevel Pulsed Modulation Technique for Low Power High Data Rate Optical Biotelemetry

, IEEE Biomedical Circuits and Systems Conference (IEEE BioCAS), Publisher: IEEE

Conference paper

Cavallo FR, Mirza KB, de Mateo S, Manzano JR, Nikolic K, Toumazou Cet al., 2021,

A Point-of-Care Device for Sensitive Protein Quantification

, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302

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Citations: 2

Conference paper

Han Z, Francesca C, Nikolic K, Mirza K, Toumazou Cet al., 2021,

Signal Identification of DNA Amplification Curves in Custom-PCR Platforms

, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302

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Citations: 6

Conference paper

Jahin M, Fenech-Salerno B, Moser N, Georgiou P, Flanagan J, Toumazou C, De Mateo S, Kalofonou Met al., 2021,

Detection of <i>MGMT</i> methylation status using a Lab-on-Chip compatible isothermal amplification method

, 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC), Publisher: IEEE, Pages: 7385-7389, ISSN: 1557-170X

Conference paper

Toth R, Zamora M, Ottaway J, Gillbe T, Martin S, Benjaber M, Lamb G, Noone T, Taylor B, Deli A, Kremen V, Worrell G, Constandinou TG, Gillbe I, De Wachter S, Knowles C, Sharott A, Valentin A, Green AL, Denison Tet al., 2020,

DyNeuMo Mk-2: an investigational circadian-locked neuromodulator with responsive stimulation for applied chronobiology

, 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Publisher: IEEE, Pages: 3433-3440, ISSN: 0884-3627

Deep brain stimulation (DBS) for Parkinson's disease, essential tremor and epilepsy is an established palliative treatment. DBS uses electrical neuromodulation to suppress symptoms. Most current systems provide a continuous pattern of fixed stimulation, with clinical follow-ups to refine settings constrained to normal office hours. An issue with this management strategy is that the impact of stimulation on circadian, i.e. sleep-wake, rhythms is not fully considered; either in the device design or in the clinical follow-up. Since devices can be implanted in brain targets that couple into the reticular activating network, impact on wakefulness and sleep can be significant. This issue will likely grow as new targets are explored, with the potential to create entraining signals that are uncoupled from environmental influences. To address this issue, we have designed a new brain-machine-interface for DBS that combines a slow-adaptive circadian-based stimulation pattern with a fast-acting pathway for responsive stimulation, demonstrated here for seizure management. In preparation for first-in-human research trials to explore the utility of multi-timescale automated adaptive algorithms, design and prototyping was carried out in line with ISO risk management standards, ensuring patient safety. The ultimate aim is to account for chronobiology within the algorithms embedded in brain-machine-interfaces and in neuromodulation technology more broadly.

Journal article

Wildner K, Mirza KB, De La Franier B, Cork S, Toumazou C, Thompson M, Nikolic Ket al., 2020,

Iridium oxide based potassium sensitive microprobe with anti-fouling properties

, IEEE Sensors Journal, Vol: 20, Pages: 12610-12619, ISSN: 1530-437X

Here, we present a new type of potassium sensor which possesses a combination of potassium sensing and anti-biofouling properties. Two major advancements were required to be developed with respect to the current technology; Firstly, design of surface linkers for this type of coating that would allow deposition of the potassiumselective coating on Iridium (Ir) wire or micro-spike surface for chronic monitoring for the first time. As this has never been done before, even for flat Ir surfaces, the material’s small dimensions and surface area render this challenging. Secondly, the task of transformation of the coated wire into a sensor. Here we develop and bench-test the electrode sensitivity to potassium and determine its specificity to potassium versus sodium interference. For this purpose we also present a novel characterisation platform which enables dynamic characterization of the sensor including step and sinusoidal response to analyte changes. The developed sensor shows good sensitivity (<1 mM concentrations of K+ ions) and selectivity (up to approximately 10 times more sensitive to K+ than Na+ concentration changes, depending on concentrations and ionic environment). In addition, the sensor displays very good mechanical properties for the small diameter involved (sub 150 μm), which in combination with anti-biofouling properties, renders it an excellent potential tool for the chemical monitoring of neural and other physiological activities using implantable devices.

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, 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.

Journal article

Liu X, Chen C-H, Karvela M, Toumazou Cet al., 2020,

A DNA-Based Intelligent Expert System for Personalised Skin-Health Recommendations

, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 24, Pages: 3276-3284, ISSN: 2168-2194

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Citations: 7

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