249 results found
Rapeaux A, Syed O, Cuttaz E, et 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.
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.
Zamora M, Toth R, Morgante F, et 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
Ahmadi N, Adiono T, Purwarianti A, et 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.
Lauteslager T, Tommer M, Lande TS, et al., 2022, Dynamic Microwave Imaging of the Cardiovascular System Using Ultra-Wideband Radar-on-Chip Devices., IEEE Trans Biomed Eng, Vol: PP
OBJECTIVE: Microwave imaging has been investigated for medical applications such as stroke and breast imaging. Current systems typically rely on bench-top equipment to scan at a variety of antenna positions. For dynamic imaging of moving structures, such as the cardiovascular system, much higher imaging speeds are required than what has thus far been reported. Recent innovations in radar-on-chip technology allow for simultaneous high speed data collection at multiple antenna positions at a fraction of the cost of conventional microwave equipment, in a small and potentially portable system. The objective of the current work is to provide proof of concept of dynamic microwave imaging in the body, using radar-on-chip technology. METHODS: Arrays of body-coupled antennas were used with nine simultaneously operated coherent ultra-wideband radar chips. Data were collected from the chest and thigh of a volunteer, with the objective of imaging the femoral artery and beating heart. In addition, data were collected from a phantom to validate system performance. Video data were constructed using beamforming. RESULTS: The location of the femoral artery could successfully be resolved, and a distinct arterial pulse wave was discernable. Cardiac activity was imaged at locations corresponding to the heart, but image quality was insufficient to identify individual anatomical structures. Static and differential imaging of the femur bone proved unsuccessful. CONCLUSION: Using radar chip technology and an imaging approach, cardiovascular activity was detected in the body, demonstrating first steps towards biomedical dynamic microwave imaging. The current portable and modular system design was found unsuitable for static in-body imaging. SIGNIFICANCE: This first proof of concept demonstrates that radar-on-chip could enable cardiovascular imaging in a low-cost, small and portable system. Such a system could make medical imaging more accessible, particularly in ambulatory or long-term moni
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
Teversham J, Wong SS, Hsieh B, et al., 2022, Development of an Ultra Low-Cost SSVEP-based BCI Device for Real-Time On-Device Decoding
<jats:title>Abstract</jats:title><jats:p>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.</jats:p>
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>
Rapeaux AB, Constandinou TG, 2021, Implantable brain machine interfaces: first-in-human studies, technology challenges and trends, CURRENT OPINION IN BIOTECHNOLOGY, Vol: 72, Pages: 102-111, ISSN: 0958-1669
Maheshwari S, Stathopoulos S, Wang J, et al., 2021, Design flow for hybrid CMOS/memristor systems--Part I: modeling and verification steps, IEEE Transactions on Circuits and Systems I: Regular Papers, Vol: 68, Pages: 4862-4875, ISSN: 1549-8328
Memristive technology has experienced explosive growth in the last decade, with multiple device structures being developed for a wide range of applications. However, transitioning the technology from the lab into the marketplace requires the development of an accessible and user-friendly design flow, supported by an industry-grade toolchain. In this work, we demonstrate the behaviour of our in-house fabricated custom memristor model and its integration into the Cadence Electronic Design Automation (EDA) tools for verification. Various input stimuli were given to record the memristive device characteristics both at the device level as well as the schematic level for verification of the memristor model. This design flow from device to industrial level EDA tools is the first step before the model can be used and integrated with Complementary Metal-Oxide Semiconductor (CMOS) in applications for hybrid memristor/CMOS system design.
Maheshwari S, Stathopoulos S, Wang J, et al., 2021, Design flow for hybrid CMOS/memristor systems--Part II: circuit schematics and layout, IEEE Transactions on Circuits and Systems I: Regular Papers, Vol: 68, Pages: 4876-4888, ISSN: 1549-8328
\normalsize The capability of in-memory computation, reconfigurability, low power operation as well as multistate operation of the memristive device deems them a suitable candidate for designing electronic circuits with a broad range of applications. Besides, the integrability of memristor with CMOS enables it to use in logic circuits too. In this work, we demonstrate with examples the design flow for memristor-based electronics, after the custom memristor model already being integrated and validated into our chosen Computer-Aided Design (CAD) tool to performing layout-versus-schematic and post-layout checks including the memristive device. We envisage that this step-by-step guide to introducing memristor into the standard integrated circuit design flow will be a useful reference document for both device developers who wish to benchmark their technologies and circuit designers who wish to experiment with memristive-enhanced systems.
Harding EC, Ba W, Zahir R, et 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, ISSN: 1662-453X
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 and REM active, especially at the boundary of wake to NREM transitions, and in the later parts of REM bouts, with lower activity during wakefulness. 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 h, 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 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.
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.
Antoniadis DD, Feng P, Mifsud A, et 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.
Neuromodulation is an established treatment for numerous neurological conditions, but to expand the therapeutic scope there is a need to improve the spatial, temporal and cell-type specificity of stimulation. Optogenetics is a promising area of current research, enabling optical stimulation of genetically-defined cell types without interfering with concurrent electrical recording for closed-loop control of neural activity. We are developing an open-source system to provide a platform for closed-loop optogenetic neuromodulation, incorporating custom integrated circuitry for recording and stimulation, real-time closed-loop algorithms running on a microcontroller and experimental control via a PC interface. We include commercial components to validate performance, with the ultimate aim of translating this approach to humans. In the meantime our system is flexible and expandable for use in a variety of preclinical neuroscientific applications. The platform consists of a Controlling Abnormal Network Dynamics using Optogenetics (CANDO) Control System (CS) that interfaces with up to four CANDO headstages responsible for electrical recording and optical stimulation through custom CANDO LED optrodes. Control of the hardware, inbuilt algorithms and data acquisition is enabled via the CANDO GUI (Graphical User Interface). Here we describe the design and implementation of this system, and demonstrate how it can be used to modulate neuronal oscillations in vitro and in vivo.
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
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.
Chen Z, Bannon A, Rapeaux A, et 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.
Harding EC, Ba W, Zahir R, et 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.
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.
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.
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.
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.
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.
Feng P, Constandinou TG, 2021, Autonomous Wireless System for Robust and Efficient Inductive Power Transmission to Multi-Node Implants, Publisher: Cold Spring Harbor Laboratory
<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>
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
Feng P, Constandinou TG, 2021, Autonomous Wireless System for Robust and Efficient Inductive Power Transmission to Multi-Node Implants, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
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
Tringali D, Haci D, Mazza F, et 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
Del Bono F, Rapeaux A, Demarchi D, et 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
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