Imperial College London

Professor Timothy Constandinou

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Bioelectronics
 
 
 
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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
to

222 results found

Maheshwari S, Stathopoulos S, Wang J, Serb A, Pan Y, Mifsud A, Leene LB, Shen J, Papavassiliou C, Constandinou TG, Prodromakis Tet al., 2021, Design Flow for Hybrid CMOS/Memristor Systems--Part I: Modeling and Verification Steps, IEEE Transactions on Circuits and Systems I: Regular Papers, Pages: 1-14, ISSN: 1549-8328

Journal article

Maheshwari S, Stathopoulos S, Wang J, Serb A, Pan Y, Mifsud A, Leene LB, Shen J, Papavassiliou C, Constandinou TG, Prodromakis Tet al., 2021, Design Flow for Hybrid CMOS/Memristor Systems--Part II: Circuit Schematics and Layout, IEEE Transactions on Circuits and Systems I: Regular Papers, Pages: 1-13, ISSN: 1549-8328

Journal article

Rapeaux AB, Constandinou TG, 2021, Implantable brain machine interfaces: first-in-human studies, technology challenges and trends., Curr Opin Biotechnol, Vol: 72, Pages: 102-111

Implantable brain machine interfaces (BMIs) are now on a trajectory to go mainstream, wherein what was once considered last resort will progressively become elective at earlier stages in disease treatment. First-in-human successes have demonstrated the ability to decode highly dexterous motor skills such as handwriting, and speech from human cortical activity. These have been used for cursor and prosthesis control, direct-to-text communication and speech synthesis. Along with these breakthrough studies, technology advancements have enabled the observation of more channels of neural activity through new concepts for centralised/distributed implant architectures. This is complemented by research in flexible substrates, packaging, surgical workflows and data processing. New regulatory guidance and funding has galvanised the field. This culmination of resource, efforts and capability is now attracting significant investment for BMI commercialisation. This paper reviews recent developments and describes the paradigm shift in BMI development that is leading to new innovations, insights and BMI translation.

Journal article

Harding EC, Ba W, Zahir R, Yu X, Yustos R, Hsieh B, Lignos L, Vyssotski AL, Merkle FT, Constandinou TG, Franks NP, Wisden Wet al., 2021, Nitric Oxide Synthase Neurons in the Preoptic Hypothalamus Are NREM and REM Sleep-Active and Lower Body Temperature, FRONTIERS IN NEUROSCIENCE, Vol: 15

Journal article

Ahmadi N, Constandinou T, Bouganis C, 2021, Inferring entire spiking activity from local field potentials, Scientific Reports, Vol: 11, Pages: 1-13, ISSN: 2045-2322

Extracellular recordings are typically analysed by separating them into two distinct signals: local field potentials (LFPs) andspikes. Previous studies have shown that spikes, in the form of single-unit activity (SUA) or multiunit activity (MUA), can beinferred solely from LFPs with moderately good accuracy. SUA and MUA are typically extracted via threshold-based techniquewhich may not be reliable when the recordings exhibit a low signal-to-noise ratio (SNR). Another type of spiking activity, referredto as entire spiking activity (ESA), can be extracted by a threshold-less, fast, and automated technique and has led to betterperformance in several tasks. However, its relationship with the LFPs has not been investigated. In this study, we aim toaddress this issue by inferring ESA from LFPs intracortically recorded from the motor cortex area of three monkeys performingdifferent tasks. Results from long-term recording sessions and across subjects revealed that ESA can be inferred from LFPswith good accuracy. On average, the inference performance of ESA was consistently and significantly higher than those of SUAand MUA. In addition, local motor potential (LMP) was found to be the most predictive feature. The overall results indicate thatLFPs contain substantial information about spiking activity, particularly ESA. This could be useful for understanding LFP-spikerelationship and for the development of LFP-based BMIs.

Journal article

Constandinou TG, Tang KT, Wang G, 2021, Editorial Special Section on Selected Papers From ISICAS 2020, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 15, Pages: 646-646, ISSN: 1932-4545

Journal article

Antoniadis DD, Feng P, Mifsud A, Constandinou TGet al., 2021, Open-source memory compiler for automatic RRAM generation and verification, 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), Publisher: IEEE, Pages: 97-100

The lack of open-source memory compilers in academia typically causes significant delays in research and design implementations. This paper presents an open-source memory compiler that is directly integrated within the Cadence Virtuoso environment using physical verification tools provided by Mentor Graphics (Calibre). It facilitates the entire memory generation process from netlist generation to layout implementation, and physical implementation verification. To the best of our knowledge, this is the first open-source memory compiler that has been developed specifically to automate Resistive Random Access Memory (RRAM) generation. RRAM holds the promise of achieving high speed, high density and non-volatility. A novel RRAM architecture, additionally is proposed, and a number of generated RRAM arrays are evaluated to identify their worst case control line parasitics and worst case settling time across the memristors of their cells. The total capacitance of lines SEL, N and P is 5.83 fF/cell, 3.31 fF/cell and 2.48 fF/cell respectively, while the total calculated resistance for SEL is 1.28 Ω/cell and 0.14 Ω/cell for both N and P lines.

Conference paper

Firfilionis D, Hutchings F, Tamadoni R, Walsh D, Turnbull M, Escobedo-Cousin E, Bailey RG, Gausden J, Patel A, Haci D, Liu Y, LeBeau FEN, Trevelyan A, Constandinou TG, O'Neill A, Kaiser M, Degenaar P, Jackson Aet al., 2021, A Closed-Loop Optogenetic Platform, FRONTIERS IN NEUROSCIENCE, Vol: 15

Journal article

Szostak KM, Keshavarz M, Constandinou T, 2021, Hermetic chip-scale packaging using Au:Sn eutectic bonding for implantable devices, Journal of Micromechanics and Microengineering, Vol: 31, Pages: 1-13, ISSN: 0960-1317

Advancements in miniaturisation and new capabilities of implantable devices impose a need for the development of compact, hermetic, and CMOS-compatible micro packaging methods. Gold-tin-based eutectic bonding presents the potential for achieving low-footprint seals with low permeability to moisture at process temperatures below 350 compfnC. This work describes a method for the deposition of Au:Sn eutectic alloy frames by sequential electroplating from commercially available solutions. Frames were bonded on the chip-level in the process of eutectic bonding. Bond quality was characterised through shear force measurements, scanning electron microscopy, visual inspection, and immersion tests. Characterisation of seals geometry, solder thickness, and bonding process parameters was evaluated, along with toxicity assessment of bonding layers to the human fibroblast cells. With a successful bond yield of over 70% and no cytotoxic effect, Au:Sn eutectic bonding appears as a suitable method for the protection of integrated circuitry in implantable applications.

Journal article

Chen Z, Bannon A, Rapeaux A, Constandinou TGet al., 2021, Towards robust, unobtrusive sensing of respiration using UWB impulse Radar for the care of people living with dementia, 10th International IEEE-EMBS Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 866-871, ISSN: 1948-3546

The unobtrusive monitoring of vital signals and behaviour can be used to gather intelligence to support the care of people living with dementia. This can provide insights into the person's wellbeing and the neurogenerative process, as well as enable them to continue to live safely at home, thereby improving their quality of life. Within this context, this study investigated the deployability of non-contact respiration rate (RR) measurement based on an Ultra-Wideband (UWB) radar System-on-Chip (SoC). An algorithm was developed to simultaneously and continuously extract the respiration signal, together with the confidence level of the respiration signal and the target position, without needing any prior calibration. The radar-measured RR results were compared to the RR results obtained from a ground truth measure based on the breathing sound, and the error rates were within 8% with a mean value of 2.5%. The target localisation results match to the radar-to-chest distances with a mean error rate of 5.8%. The tested measurement range was up to 5m. The results suggest that the algorithm could perform sufficiently well in non-contact stationary respiration rate detection.

Conference paper

Harding EC, Ba W, Zahir R, Yu X, Yustos R, Hsieh B, Lignos L, Vyssotski AL, Constandinou T, Franks NP, Wisden Wet al., 2021, Nitric oxide synthase neurons in the preoptic hypothalamus are sleep-active and contribute to regulating NREM and REM sleep and lowering body temperature, Publisher: Cold Spring Harbor Laboratory

When mice are exposed to external warmth, nitric oxide synthase (NOS1) neurons in the median and medial preoptic (MnPO/MPO) hypothalamus induce sleep and concomitant body cooling. However, how these neurons regulate baseline sleep and body temperature is unknown. Using calcium photometry, we show that NOS1 neurons in MnPO/MPO are predominantly NREM active. This is the first instance of a predominantly NREM-active population in the PO area, or to our knowledge, elsewhere in the brain. In addition to releasing nitric oxide, NOS1 neurons in MnPO/MPO can release GABA, glutamate and peptides. We expressed tetanus-toxin light-chain in MnPO/MPO NOS1 cells to reduce vesicular release of transmitters. This induced changes in sleep structure: over 24 hours, mice had less NREM sleep in their dark (active) phase, and more NREM sleep in their light (sleep) phase. REM sleep episodes in the dark phase were longer, and there were fewer REM transitions between other vigilance states. REM sleep had less theta power. Mice with synaptically blocked MnPO/MPO NOS1 neurons were also warmer. In particular, mice were warmer than control mice at the dark-light transition (ZT0), as well as during the dark phase siesta (ZT16-20), where there is usually a body temperature dip. Also, at this siesta point of cooled body temperature, mice usually have more NREM, but mice with synaptically blocked MnPO/MPO NOS1 cells showed reduced NREM sleep at this time. Overall, MnPO/MPO NOS1 neurons promote both NREM and REM sleep and contribute to chronically lowering body temperature, particularly at transitions where the mice normally enter NREM sleep.

Working paper

Zhang Z, Constandinou TG, 2021, A robust and automated algorithm that uses single-channel spike sorting to label multi-channel Neuropixels data, 10th International IEEE-EMBS Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 783-787, ISSN: 1948-3546

This paper describes preliminary work towards an automated algorithm for labelling Neuropixel data that exploits the fact that adjacent recording sites are spatially oversampled. This is achieved by combining classical single channel spike sorting with spatial spike grouping, resulting in an improvement in both accuracy and robustness. This is additionally complemented by an automated method for channel selection that determines which channels contain high quality data. The algorithm has been applied to a freely accessible dataset, produced by Cortex Lab, UCL. This has been evaluated to have a accuracy of over 77% compared to a manually curated ground truth.

Conference paper

Savolainen OW, Constandinou TG, 2021, Investigating the effects of macaque primary motor cortex multi-unit activity binning period on behavioural decoding performance, 10th International IEEE-EMBS Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 436-439, ISSN: 1948-3546

This paper investigates the relationship between Multi-Unit Activity (MUA) Binning Period (BP) and Brain-Computer Interface (BCI) decoding performance using Long-Short Term Memory decoders. The motivation is to determine whether lossy compression of MUA via increasing BP has any adverse consequences for BCI Behavioral Decoding Performance (BDP). The Neural data originates from intracortical recordings from Macaque Primary Motor cortex. The BDP is measured by the Pearson correlation r between the observed and predicted velocity of the subject's X- Y hand coordinates in reaching tasks. The results suggest a statistically significant but slight linear relationship between increasing MUA BP and decreasing BDP. For example, when using a 100 ms moving average window, increasing the BP by 10 ms on average reduces the BDP r by approximately 0.85%. This relationship may be due to the reduced number of training examples, or due to the loss of Behavioral information because of reduced MUA temporal resolution.

Conference paper

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

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.

Journal article

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.

Conference paper

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

Thesis dissertation

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

Williams I, Brunton E, Rapeaux A, Liu Y, Luan S, Nazarpour K, Constandinou TGet 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.

Journal article

Zamora M, Toth R, Morgante F, Ottaway J, Gillbe T, Martin S, Lamb G, Noone T, Benjaber M, Nairac Z, Constandinou T, Herron J, Aziz T, Gillbe I, Green A, Pereira E, Denison Tet 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.

Working paper

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

Journal article

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.

Conference paper

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.

Conference paper

Tossell K, Yu X, Soto BA, Vicente M, Miracca G, Giannos P, Miao A, Hsieh B, Ma Y, Yustos R, Vyssotski A, Constandinou T, Franks N, Wisden Wet 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.

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

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.

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

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

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

Conference paper

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