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  • CONFERENCE PAPER
    Ahmadi N, Constandinou T, Bouganis C, 2019,

    Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network

    , 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER 2019), Pages: 1-5

    Local eld potential (LFP) has gained increasing interest as an alternative input signal for brain-machine interfaces (BMIs) due to its informative features, long-term stability, and low frequency content. However, despite these interesting properties, LFP-based BMIs have been reported to yield low decoding performances compared to spike-based BMIs. In this paper, we propose a new decoder based on long short-term memory (LSTM) network which aims to improve the decoding performance of LFP-based BMIs. We compare of ine decoding performance of the proposed LSTM decoder to a commonly used Kalman lter (KF) decoder on hand kinematics prediction tasks from multichannel LFPs. We also benchmark the performance of LFP-driven LSTM decoder against KF decoder driven by two types of spike signals: singleunit activity (SUA) and multi-unit activity (MUA). Our results show that LFP-driven LSTM decoder achieves signi cantly better decoding performance than LFP-, SUA-, and MUAdrivenKF decoders. This suggests that LFPs coupled with LSTM decoder could provide high decoding performance, robust, and low power BMIs.

  • CONFERENCE PAPER
    Ahmadi N, Cavuto M, Feng P, Leene L, Maslik M, Mazza F, Savolainen O, Szostak K, Bouganis C, Ekanayake J, Jackson A, Constandinou Tet al., 2019,

    Towards a Distributed, Chronically-Implantable Neural Interface

    , IEEE/EMBS Conference on Neural Engineering (NER), Pages: 1-6

    We present a platform technology encompassing a family of innovations that together aim to tackle key challenges with existing implantable brain machine interfaces. The ENGINI (Empowering Next Generation Implantable Neural Interfaces) platform utilizes a 3-tier network (external processor, cranial transponder, intracortical probes) to inductively couple power to, and communicate data from, a distributed array of freely-floating mm-scale probes. Novel features integrated into each probe include: (1) an array of niobium microwires for observing local field potentials (LFPs) along the cortical column; (2) ultra-low power instrumentation for signal acquisition and data reduction; (3) an autonomous, self-calibrating wireless transceiver for receiving power and transmitting data; and (4) a hermetically-sealed micropackage suitable for chronic use. We are additionally engineering a surgical tool, to facilitate manual and robot-assisted insertion, within a streamlined neurosurgical workflow. Ongoing work is focused on system integration and preclinical testing.

  • JOURNAL ARTICLE
    Ahmadi N, Constandinou TG, Bouganis C-S, 2018,

    Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS)

    , PLOS ONE, Vol: 13, ISSN: 1932-6203
  • CONFERENCE PAPER
    Moly A, Luan S, Zoltan M, Salimpour Y, Anderson W, Constandinou TG, Grand Let al., 2018,

    Embedded Phase-Amplitude Coupling Based Closed-Loop Platform for Parkinson's Disease

    , IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 527-530

    Deep Brain Stimulation (DBS) is a widely used clin-ical therapeutic modality to treat Parkinsons disease refractorysymptoms and complications of levodopa therapy. Currentlyavailable DBS systems use continuous, open-loop stimulationstrategies. It might be redundant and we could extend the batterylife otherwise. Recently, robust electrophysiological signaturesof Parkinsons disease have been characterized in motor cortexof patients undergoing DBS surgery. Reductions in the beta-gamma Phase-Amplitude coupling (PAC) correlated with symp-tom improvement, and the therapeutic effects of DBS itself. Weaim to develop a miniature, implantable and adaptive system,which only stimulates the neural target, when triggered by theoutput of the appropriate PAC algorithm. As a first step, in thispaper we compare published PAC algorithms by using humandata intra-operatively recorded from Parkinsonian patients. Wethen introduce IIR masking for later achieving fast and low-power FPGA implementation of PAC mapping for intra-operativestudies. Our closed-loop application is expected to consumesignificantly less power than current DBS systems, thereforewe can increase the battery life, without compromising clinicalbenefits.

  • JOURNAL ARTICLE
    Feng P, Yeon P, Cheng Y, Ghovanloo M, Constandinou TGet al., 2018,

    Chip-Scale Coils for Millimeter-Sized Bio-Implants

    , IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 12, Pages: 1088-1099, ISSN: 1932-4545
  • JOURNAL ARTICLE
    Leene LB, Constandinou TG, 2018,

    A 0.006 mm(2) 1.2 mu W Analog-to-Time Converter for Asynchronous Bio-Sensors

    , IEEE JOURNAL OF SOLID-STATE CIRCUITS, Vol: 53, Pages: 2604-2613, ISSN: 0018-9200
  • JOURNAL ARTICLE
    Luan S, Williams I, Maslik M, Liu Y, De Carvalho F, Jackson A, Quiroga RQ, Constandinou TGet al., 2018,

    Compact standalone platform for neural recording with real-time spike sorting and data logging

    , JOURNAL OF NEURAL ENGINEERING, Vol: 15, ISSN: 1741-2560
  • JOURNAL ARTICLE
    Troiani F, Nikolic K, Constandinou TG, 2018,

    Simulating optical coherence tomography for observing nerve activity: a finite difference time domain bi-dimensional model

    , PLoS ONE, Vol: 13, Pages: 1-14, ISSN: 1932-6203

    We present a finite difference time domain (FDTD) model for computation of A line scans in time domain optical coherence tomography (OCT). The OCT output signal is created using two different simulations for the reference and sample arms, with a successive computation of the interference signal with external software. In this paper we present the model applied to two different samples: a glass rod filled with water-sucrose solution at different concentrations and a peripheral nerve. This work aims to understand to what extent time domain OCT can be used for non-invasive, direct optical monitoring of peripheral nerve activity.

  • CONFERENCE PAPER
    Ahmadi N, Constandinou TG, Bouganis C-S, 2018,

    Spike Rate Estimation Using Bayesian Adaptive Kernel Smoother (BAKS) and Its Application to Brain Machine Interfaces.

    , Pages: 2547-2550, ISSN: 1557-170X

    Brain Machine Interfaces (BMIs) mostly utilise spike rate as an input feature for decoding a desired motor output as it conveys a useful measure to the underlying neuronal activity. The spike rate is typically estimated by a using non-overlap binning method that yields a coarse estimate. There exist several methods that can produce a smooth estimate which could potentially improve the decoding performance. However, these methods are relatively computationally heavy for real-time BMIs. To address this issue, we propose a new method for estimating spike rate that is able to yield a smooth estimate and also amenable to real-time BMIs. The proposed method, referred to as Bayesian adaptive kernel smoother (BAKS), employs kernel smoothing technique that considers the bandwidth as a random variable with prior distribution which is adaptively updated through a Bayesian framework. With appropriate selection of prior distribution and kernel function, an analytical expression can be achieved for the kernel bandwidth. We apply BAKS and evaluate its impact on offline BMI decoding performance using Kalman filter. The results reveal that BAKS can improve the decoding performance compared to the binning method. This suggests the feasibility and the potential use of BAKS for real-time BMIs.

  • JOURNAL ARTICLE
    Ramezani R, Liu Y, Dehkhoda F, Soltan A, Haci D, Zhao H, Firfilionis D, Hazra A, Cunningham MO, Jackson A, Constandinou TG, Degenaar Pet al., 2018,

    On-Probe Neural Interface ASIC for Combined Electrical Recording and Optogenetic Stimulation

    , IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 12, Pages: 576-588, ISSN: 1932-4545
  • JOURNAL ARTICLE
    Maslik M, Liu Y, Lande TS, Constandinou TGet al., 2018,

    Continuous-Time Acquisition of Biosignals Using a Charge-Based ADC Topology

    , IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 12, Pages: 471-482, ISSN: 1932-4545
  • CONFERENCE PAPER
    Szostak KM, Constandinou TG, 2018,

    Hermetic packaging for implantable microsystems: effectiveness of sequentially electroplated AuSn alloy

    , 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE

    With modern microtechnology, there is an aggressive miniaturization of smart devices, despite an increasing level of integration and overall complexity. It is therefore becoming increasingly important to be achieve reliable, compact packaging. For implantable medical devices (IMDs), the package must additionally provide a high quality hermetic environmentto protect the device from the human body. For chip-scale devices, AuSn eutectic bonding offers the possibility of forming compact seals that achieve ultra-low permeability. A key feature is this can be achieved at process temperatures of below 350 C, therefore allowing for the integration of sensors and microsystems with CMOS electronics within a single package. Issueshowever such as solder wetting, void formation and controlling composition make formation of high-quality repeatable seals highly challenging. Towards this aim, this paper presents our experimental work characterizing the eutectic stack deposition. We detail our design methods and process flow, share our experiences in controlling electrochemical deposition of AuSnalloy and finally discuss usability of sequential electroplating process for the formation of hermetic eutectic bonds.

  • CONFERENCE PAPER
    Rapeaux A, Brunton E, Nazarpour K, Constandinou TGet al., 2018,

    Preliminary study of time to recovery of rat sciatic nerve from high frequency alternating current nerve block

    , 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE

    High-Frequency alternating current nerve block has great potential for neuromodulation-based therapies. However, no precise measurements have been made of the time needed for nerves to recover from block once the signal has been turned off. This study aims to characterise time to recoveryof the rat sciatic nerve after 30 seconds of block at varying amplitudes and frequencies. Experiments were carried out in-vivo to quantify recovery times and recovery completeness within 0.7s from the end of block. The sciatic nerve was blocked with an alternating square wave signal of amplitudeand frequency ranging from 2 to 9mA and 10 to 50 kHz respectively. To determine the recovery dynamics the nerve was stimulated at 100 Hz after cessation of the blocking stimulus. Electromyogram signals were measured from the gastrocnemius medialis and tibialis anterior muscles during trials as indicators of nerve function. This allowed for nerve recovery to bemeasured with a resolution of 10 ms. This resolution is much greater than previous measurements of nerve recovery in the literature. Times for the nerve to recover to a steady state of activity ranged from 20 to 430 milliseconds and final relative recovery activity at 0.7 seconds spanned 0.2 to 1 approximately. Higher blocking signal amplitudes increased recovery time and decreased recovery completeness. These results suggestthat blocking signal properties affect nerve recovery dynamics, which could help improve neuromodulation therapies and allow more precise comparison of results across studies using different blocking signal parameters.

  • JOURNAL ARTICLE
    Liu Y, Pereira JL, Constandinou TG, 2018,

    Event-driven processing for hardware-efficient neural spike sorting

    , JOURNAL OF NEURAL ENGINEERING, Vol: 15, ISSN: 1741-2560
  • CONFERENCE PAPER
    Mazza F, Liu Y, Donaldson N, Constandinou TGet al., 2018,

    Integrated Devices for Micro-Package Integrity Monitoring in mm-Scale Neural Implants

    , IEEE Biomedical Circuits and Systems Conference (BioCAS) - Advanced Systems for Enhancing Human Health, Publisher: IEEE, Pages: 295-298, ISSN: 2163-4025
  • CONFERENCE PAPER
    Haci D, Liu Y, Nikolic K, Demarchi D, Constandinou TG, Georgiou Pet al., 2018,

    Thermally Controlled Lab-on-PCB for Biomedical Applications

    , IEEE Biomedical Circuits and Systems Conference (BioCAS) - Advanced Systems for Enhancing Human Health, Publisher: IEEE, Pages: 655-658, ISSN: 2163-4025
  • CONFERENCE PAPER
    Haci D, Liu Y, Ghoreishizadeh SS, Constandinou TGet al., 2018,

    Design Considerations for Ground Referencing in Multi-Module Neural Implants

    , IEEE Biomedical Circuits and Systems Conference (BioCAS) - Advanced Systems for Enhancing Human Health, Publisher: IEEE, Pages: 563-566, ISSN: 2163-4025
  • CONFERENCE PAPER
    Feng P, Constandinou TG, 2018,

    Robust Wireless Power Transfer to Multiple mm-Scale Freely-Positioned Neural Implants

    , IEEE Biomedical Circuits and Systems Conference (BioCAS) - Advanced Systems for Enhancing Human Health, Publisher: IEEE, Pages: 363-366, ISSN: 2163-4025
  • CONFERENCE PAPER
    Leene LB, Constandinou TG, 2018,

    Direct Digital Wavelet Synthesis for Embedded Biomedical Microsystems

    , IEEE Biomedical Circuits and Systems Conference (BioCAS) - Advanced Systems for Enhancing Human Health, Publisher: IEEE, Pages: 77-80, ISSN: 2163-4025
  • CONFERENCE PAPER
    Maslik M, Lande TSB, Constandinou TG, 2018,

    A Clockless Method of Flicker Noise Suppression in Continuous-Time Acquisition of Biosignals

    , IEEE Biomedical Circuits and Systems Conference (BioCAS) - Advanced Systems for Enhancing Human Health, Publisher: IEEE, Pages: 491-494, ISSN: 2163-4025

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