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  • Journal article
    Savolainen OW, Zhang Z, Constandinou TG, 2022,

    Ultra Low Power, Event-Driven Data Compression of Multi-Unit Activity

    <jats:title>Abstract</jats:title><jats:p>Recent years have demonstrated the feasibility of using intracortical Brain-Machine Interfaces (iBMIs), by decoding thoughts, for communication and cursor control tasks. iBMIs are increasingly becoming wireless due to the risk of infection and mechanical failure, typically associated with percutaneous connections. The wireless communication itself, however, increases the power consumption further; with the total dissipation being strictly limited due to safety heating limits of cortical tissue. Since wireless power is typically proportional to the communication bandwidth, the output Bit Rate (BR) must be minimised. Whilst most iBMIs utilise Multi-Unit activity (MUA), i.e. spike events, and this in itself significantly reduces the output BR (compared to raw data), it still limits the scalability (number of channels) that can be achieved. As such, additional compression for MUA signals are essential for fully-implantable, high-information-bandwidth systems. To meet this need, this work proposes various hardware-efficient, ultra-low power MUA compression schemes. We investigate them in terms of their BRs and hardware requirements as a function of various on-implant conditions such as MUA Binning Period (BP) and number of channels. It was found that for BPs ≤ 10 ms, the delta-asynchronous method had the lowest total power and reduced the BR by almost an order of magnitude relative to classical methods (e.g. to approx. 151 bps/channel for a BP of 1 ms and 1000 channels on-implant.). However, at larger BPs the synchronous method performed best (e.g. approx. 29 bps/channel for a BP of 50 ms, independent of channel count). As such, this work can guide the choice of MUA data compression scheme for BMI applications, where the BR can be significantly reduced in hardware efficient ways. This enables the next generation of wireless iBMIs, with small implant sizes, high channel counts, low-power, and small hardware foo
  • Conference paper
    Mifsud A, Shen J, Feng P, Xie L, Wang C, Pan Y, Maheshwari S, Agwa S, Stathopoulos S, Wang S, Serb A, Papavassiliou C, Prodromakis T, Constandinou TGet al., 2022,

    A CMOS-based characterisation platform for emerging RRAM technologies

    , 2022 IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 75-79

    Mass characterisation of emerging memory devices is an essential step in modelling their behaviour for integration within a standard design flow for existing integrated circuit designers. This work develops a novel characterisation platform for emerging resistive devices with a capacity of up to 1 million devices on-chip. Split into four independent sub-arrays, it contains on-chip column-parallel DACs for fast voltage programming of the DUT. On-chip readout circuits with ADCs are also available for fast read operations covering 5-decades of input current (20nA to 2mA). This allows a device’s resistance range to be between 1kΩ and 10MΩ with a minimum voltage range of ±1.5V on the device.
  • Journal article
    Savolainen O, Zhang Z, Feng P, Constandinou Tet al., 2022,

    Hardware-efficient compression of neural multi-unit activity

    , IEEE Access, Vol: 10, Pages: 117515-117529, ISSN: 2169-3536

    Brain-machine interfaces (BMI) are tools for measuring neural activity in the brain, used to treat numerous conditions. It is essential that the next generation of intracortical BMIs is wireless so as to remove percutaneous connections, i.e. wires, and the associated mechanical and infection risks. This is required for the effective translation of BMIs into clinical applications and is one of the remaining bottlenecks. However, due to cortical tissue thermal dissipation safety limits, the on-implant power consumption must be strictly limited. Therefore, both the neural signal processing and wireless communication power should be minimal, while the implants should provide signals that offer high behavioural decoding performance (BDP). The Multi-Unit Activity (MUA) signal is the most common signal in modern BMIs. However, with an ever-increasing channel count, the raw data bandwidth is becoming prohibitively high due to the associated communication power exceeding the safety limits. Data compression is therefore required. To meet this need, this work developed hardware-efficient static Huffman compression schemes for MUA data. Our final system reduced the bandwidth to 27 bps/channel, compared to the standard MUA rate of 1 kbps/channel. This compression is over an order of magnitude more than has been achieved before, while using only 0.96 uW/channel processing power and 246 logic cells. Our results were verified on 3 datasets and less than 1% loss in BDP was observed. As such, with the use of effective data compression, an order more of MUA channels can be fitted on-implant, enabling the next generation of high-performance wireless intracortical BMIs.
  • Journal article
    Zaaimi B, Turnbull M, Hazra A, Wang Y, Gandara C, McLeod F, McDermott EE, Escobedo-Cousin E, Idil AS, Bailey RG, Tardio S, Patel A, Ponon N, Gausden J, Walsh D, Hutchings F, Kaiser M, Cunningham MO, Clowry GJ, LeBeau FEN, Constandinou TG, Baker SN, Donaldson N, Degenaar P, O'Neill A, Trevelyan AJ, Jackson Aet al., 2023,

    Closed-loop optogenetic control of the dynamics of neural activity in non-human primates

    , NATURE BIOMEDICAL ENGINEERING, ISSN: 2157-846X
  • Conference paper
    Teversham J, Wong SS, Hsieh B, Rapeaux A, Troiani F, Savolainen O, Zhang Z, Maslik M, Constandinou TGet al., 2022,

    Development of an ultra low-cost SSVEP-based BCI device for real-time on-device decoding

    , 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Publisher: IEEE, Pages: 208-213, ISSN: 2694-0604

    This study details the development of a novel, approx. £20 electroencephalogram (EEG)-based brain-computer interface (BCI) intended to offer a financially and operationally accessible device that can be deployed on a mass scale to facilitate education and public engagement in the domain of EEG sensing and neurotechnologies. Real-time decoding of steady-state visual evoked potentials (SSVEPs) is achieved using variations of the widely-used canonical correlation analysis (CCA) algorithm: multi-set CCA and generalised CCA. All BCI functionality is executed on board an inexpensive ESP32 microcontroller. SSVEP decoding accuracy of 95.56 ± 3.74% with an ITR of 102 bits/min was achieved with modest calibration.
  • Journal article
    Lauteslager T, Tommer M, Lande TS, Constandinou TGet al., 2022,

    Dynamic Microwave Imaging of the Cardiovascular System Using Ultra-Wideband Radar-on-Chip Devices

    , IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 69, Pages: 2935-2946, ISSN: 0018-9294
  • Journal article
    Cavallo FR, Toumazou C, Nikolic K, 2022,

    Unsupervised Classification of Human Activity with Hidden Semi-Markov Models

    , APPLIED SYSTEM INNOVATION, Vol: 5
  • Journal article
    Rapeaux A, Syed O, Cuttaz E, Chapman C, Green R, Constandinou Tet al., 2022,

    Preparation of rat sciatic nerve for ex vivo neurophysiology

    , Jove-Journal of Visualized Experiments, Vol: 185, Pages: 1-14, ISSN: 1940-087X

    Ex vivo preparations enable the study of many neurophysiological processes in isolation from the rest of the body while preserving local tissue structure. This work describes the preparation of rat sciatic nerves for ex vivo neurophysiology, including buffer preparation, animal procedures, equipment setup and neurophysiological recording. This work provides an overview of the different types of experiments possible with this method. The outlined method aims to provide 6 h of stimulation and recording on extracted peripheral nerve tissue in tightly controlled conditions for optimal consistency in results. Results obtained using this method are A-fibre compound action potentials (CAP) with peak-to-peak amplitudes in the millivolt range over the entire duration of the experiment. CAP amplitudes and shapes are consistent and reliable, making them useful to test and compare new electrodes to existing models, or the effects of interventions on the tissue, such as the use of chemicals, surgical alterations, or neuromodulatory stimulation techniques. Both conventional commercially available cuff electrodes with platinum-iridium contacts and custom-made conductive elastomer electrodes were tested and gave similar results in terms of nerve stimulus strength-duration response.
  • Journal article
    Cavallo FR, Mirza KB, de Mateo S, Miglietta L, Rodriguez-Manzano J, Nikolic K, Toumazou Cet al., 2022,

    A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR

    , BIOSENSORS-BASEL, Vol: 12
  • Journal article
    Zhang Z, Constandinou TG, 2022,

    Selecting an effective amplitude threshold for neural spike detection.

    , Annu Int Conf IEEE Eng Med Biol Soc, Vol: 2022, Pages: 2328-2331

    This paper assesses and challenges whether commonly used methods for defining amplitude thresholds for spike detection are optimal. This is achieved through empirical testing of single amplitude thresholds across multiple recordings of varying SNR levels. Our results suggest that the most widely used noise-statistics-driven threshold can suffer from parameter deviation in different noise levels. The spike-noise-driven threshold can be an ideal approach to set the threshold for spike detection, which suffers less from the parameter deviation and is robust to sub-optimal settings.
  • Journal article
    Oprea A, Zhang Z, Constandinou TG, 2022,

    Hardware evaluation of spike detection algorithms towards wireless brain machine interfaces

    <jats:title>Abstract</jats:title><jats:p>The current trend for implantable Brain Machine Interfaces (BMIs) is to increase the channel count, towards next generation devices that improve on information transfer rate. This however increases the raw data bandwidth for wired or wireless systems that ultimately impacts the power budget (and thermal dissipation). On-implant feature extraction and/or compression are therefore becoming essential to reduce the data rate, however the processing power is of concern. One common feature extraction technique for intracortical BMIs is spike detection. In this work, we have empirically compared the performance, resource utilization, and power consumption of three hardware efficient spike emphasizers, Non-linear Energy Operator (NEO), Amplitude Slope Operator (ASO) and Energy of Derivative (ED), and two common statistical thresholding mechanisms (using mean or median). We also propose a novel median approximation to address the issue of the median operator not being hardware-efficient to implement. These have all been implemented and evaluated on reconfigurable hardware (FPGA) to estimate their hardware efficiency in an ultimate ASIC design. Our results suggest that ED with average thresholding provides the most hardware efficient (low power/resource) choice, while using median has the advantage of improved detection accuracy and higher robustness on threshold multiplier settings. This work is significant because it is the first to implement and compare the hardware and algorithm trade-offs that have to be made before translating the algorithms into hardware instances to design wireless implantable BMIs.</jats:p>
  • Journal article
    Rapeaux A, Constandinou T, 2022,

    HFAC dose repetition and accumulation leads to progressively longer block carryover effect in rat sciatic nerve

    , Frontiers in Neuroscience, Vol: 16, ISSN: 1662-453X

    This paper describes high-frequency nerve block experiments carried out on rat sciatic nerves to measure the speed of recoveryof A fibres from block carryover. Block carryover is the process by which nerve excitability remains suppressed temporarily afterHigh Frequency Alternative (HFAC) block is turned off following its application. In this series of experiments 5 rat sciatic nerveswere extracted and prepared for ex-vivo stimulation and recording in a specially designed perfusion chamber. For each nerverepeated HFAC block and concurrent stimulation trials were carried out to observe block carryover after signal shutoff. The nervewas allowed to recover fully between each trial. Time to recovery from block was measured by monitoring for when relativenerve activity returned to within 90% of baseline levels measured at the start of each trial. HFAC block carryover duration wasfound to be dependent on accumulated dose by statistical test for two different HFAC durations. The carryover property of HFACblock on A fibres could enable selective stimulation of autonomic nerve fibres such as C fibres for the duration of carryover. Blockcarryover is particularly relevant to potential chronic clinical applications of block as it reduces power requirements forstimulation to provide the blocking effect. This work characterises this process towards the creation of a model describing itsbehaviour.
  • Journal article
    Allsopp R, Alexandrou G, Toumazou C, Ali S, Coombes C, Kalofonou M, Shaw Jet al., 2022,

    A comparison between Mini-loop mediated isothermal amplification and polymerase spiral reaction for selective amplification of short template DNA

    , bioRxiv
  • Journal article
    Zamora M, Toth R, Morgante F, Ottaway J, Gillbe T, Martin S, Lamb G, Noone T, Benjaber M, Nairac Z, Sehgal D, Constandinou TG, Herron J, Aziz TZ, Gillbe I, Green AL, Pereira EAC, Denison Tet al., 2022,

    DyNeuMo Mk-1: Design and pilot validation of an investigational motion-adaptive neurostimulator with integrated chronotherapy

    , EXPERIMENTAL NEUROLOGY, Vol: 351, ISSN: 0014-4886
  • Journal article
    Savolainen OW, Zhang Z, Feng P, Constandinou TGet al., 2022,

    Hardware-Efficient Compression of Neural Multi-Unit Activity

    <jats:title>Abstract</jats:title><jats:p>Brain-machine interfaces (BMI) are tools for treating neurological disorders and motor-impairments. It is essential that the next generation of intracortical BMIs is wireless so as to remove percutaneous connections, i.e. wires, and the associated mechanical and infection risks. This is required for the effective translation of BMIs into clinical applications and is one of the remaining bottlenecks. However, due to cortical tissue thermal dissipation safety limits, the on-implant power consumption must be strictly limited. Therefore, both the neural signal processing and wireless communication power should be minimal, while the implants should provide signals that offer high behavioural decoding performance (BDP). The Multi-Unit Activity (MUA) signal is the most common signal in modern BMIs. However, with an ever-increasing channel count, the raw data bandwidth is becoming prohibitively high due to the associated communication power exceeding the safety limits. Data compression is therefore required. To meet this need, this work developed hardware-efficient static Huffman compression schemes for MUA data. Our final system reduced the bandwidth to 27 bps/channel, compared to the standard MUA rate of 1 kbps/channel. This compression is over an order of magnitude more than has been achieved before, while using only 0.96 uW/channel processing power and 246 logic cells. Our results were verified on 3 datasets and less than 1% loss in BDP was observed. As such, with the use of effective data compression, an order more of MUA channels can be fitted on-implant, enabling the next generation of high-performance wireless intracortical BMIs.</jats:p>
  • Journal article
    Ahmadi N, Adiono T, Purwarianti A, Constandinou T, Bouganis Cet al., 2022,

    Improved spike-based brain-machine interface using bayesian adaptive kernel smoother and deep learning

    , IEEE Access, Vol: 10, Pages: 29341-29356, ISSN: 2169-3536

    Multiunit activity (MUA) has been proposed to mitigate the robustness issue faced by single-unit activity (SUA)-based brain-machine interfaces (BMIs). Most MUA-based BMIs still employ a binning method for estimating firing rates and linear decoder for decoding behavioural parameters. The limitations of binning and linear decoder lead to suboptimal performance of MUA-based BMIs. To address this issue, we propose a method which consists of Bayesian adaptive kernel smoother (BAKS) as the firing rate estimation algorithm and deep learning, particularly quasi-recurrent neural network (QRNN), as the decoding algorithm. We evaluated the proposed method for reconstructing (offline) hand kinematics from intracortical neural data chronically recorded from the primary motor cortex of two non-human primates. Extensive empirical results across recording sessions and subjects showed that the proposed method consistently outperforms other combinations of firing rate estimation algorithm and decoding algorithm. Overall results suggest the effectiveness of the proposed method for improving the decoding performance of MUA-based BMIs.
  • Journal article
    Zhang Z, Savolainen OW, Constandinou TG, 2022,

    Algorithm and hardware considerations for real-time neural signal on-implant processing

    , JOURNAL OF NEURAL ENGINEERING, Vol: 19, ISSN: 1741-2560
  • Journal article
    Pallett SJ, Jones R, Abdulaal A, Pallett MA, Rayment M, Patel A, Denny SJ, Mughal N, Khan M, de Oliveira CR, Pantelidis P, Randell P, Toumazou C, O'Shea MK, Tedder R, McClure MO, Davies GW, Moore LSPet al., 2022,

    Variability in detection of SARS-CoV-2-specific antibody responses following mild infection: a prospective multicentre cross-sectional study, London, United Kingdom, 17 April to 17 July 2020

    , Eurosurveillance, Vol: 27, ISSN: 1025-496X

    IntroductionImmunoassays targeting different SARS-CoV-2-specific antibodies are employed for seroprevalence studies. The degree of variability between immunoassays targeting anti-nucleocapsid (anti-NP; the majority) vs the potentially neutralising anti-spike antibodies (including anti-receptor-binding domain; anti-RBD), particularly in mild or asymptomatic disease, remains unclear.AimsWe aimed to explore variability in anti-NP and anti-RBD antibody detectability following mild symptomatic or asymptomatic SARS-CoV-2 infection and analyse antibody response for correlation with symptomatology.MethodsA multicentre prospective cross-sectional study was undertaken (April–July 2020). Paired serum samples were tested for anti-NP and anti-RBD IgG antibodies and reactivity expressed as binding ratios (BR). Multivariate linear regression was performed analysing age, sex, time since onset, symptomatology, anti-NP and anti-RBD antibody BR.ResultsWe included 906 adults. Antibody results (793/906; 87.5%; 95% confidence interval: 85.2–89.6) and BR strongly correlated (ρ = 0.75). PCR-confirmed cases were more frequently identified by anti-RBD (129/130) than anti-NP (123/130). Anti-RBD testing identified 83 of 325 (25.5%) cases otherwise reported as negative for anti-NP. Anti-NP presence (+1.75/unit increase; p < 0.001), fever (≥ 38°C; +1.81; p < 0.001) or anosmia (+1.91; p < 0.001) were significantly associated with increased anti-RBD BR. Age (p = 0.85), sex (p = 0.28) and cough (p = 0.35) were not. When time since symptom onset was considered, we did not observe a significant change in anti-RBD BR (p = 0.95) but did note decreasing anti-NP BR (p < 0.001).ConclusionSARS-CoV-2 anti-RBD IgG showed significant correlation with anti-NP IgG for absolute seroconversion and BR. Higher BR were seen in symptomatic indiv
  • Journal article
    Zhang Z, Constandinou TG, 2022,

    Selecting an effective amplitude threshold for neural spike detection

    <jats:title>Abstract</jats:title><jats:p>This paper assesses and challenges whether commonly used methods for defining amplitude thresholds for spike detection are optimal. This is achieved through empirical testing of single amplitude thresholds across multiple recordings of varying SNR levels. Our results suggest that the most widely used noise-statistics-driven threshold can suffer from parameter deviation in different noise levels. The spike-noise-driven threshold can be an ideal approach to set the threshold for spike detection, which suffers less from the parameter deviation and is robust to sub-optimal settings.</jats:p>
  • Journal article
    Cavallo FR, Golden C, Pearson-Stuttard J, Falconer C, Toumazou Cet al., 2022,

    The association between sedentary behaviour, physical activity and type 2 diabetes markers: A systematic review of mixed analytic approaches

    , PLOS ONE, Vol: 17, ISSN: 1932-6203

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