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    Etard OE, Kegler M, Braiman C, Forte AE, Reichenbach JDTet al., 2018,

    Real-time decoding of selective attention from the human auditory brainstem response to continuous speech

    , BioRxiv
    Forte AE, Etard OE, Reichenbach JDT, 2018,

    Selective Auditory Attention At The Brainstem Level

    , ARO 2018
    Kegler M, Etard OE, Forte AE, Reichenbach JDTet al., 2018,

    Complex Statistical Model for Detecting the Auditory Brainstem Response to Natural Speech and for Decoding Attention from High-Density EEG Recordings

    , ARO 2018
    Liu Y, Pereira JL, Constandinou TG, 2018,

    Event-driven processing for hardware-efficient neural spike sorting

    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.

    , J Neural Eng, Vol: 15

    OBJECTIVE: Longitudinal observation of single unit neural activity from large numbers of cortical neurons in awake and mobile animals is often a vital step in studying neural network behaviour and towards the prospect of building effective brain-machine interfaces (BMIs). These recordings generate enormous amounts of data for transmission and storage, and typically require offline processing to tease out the behaviour of individual neurons. Our aim was to create a compact system capable of: (1) reducing the data bandwidth by circa 2 to 3 orders of magnitude (greatly improving battery lifetime and enabling low power wireless transmission in future versions); (2) producing real-time, low-latency, spike sorted data; and (3) long term untethered operation. APPROACH: We have developed a headstage that operates in two phases. In the short training phase a computer is attached and classic spike sorting is performed to generate templates. In the second phase the system is untethered and performs template matching to create an event driven spike output that is logged to a micro-SD card. To enable validation the system is capable of logging the high bandwidth raw neural signal data as well as the spike sorted data. MAIN RESULTS: The system can successfully record 32 channels of raw neural signal data and/or spike sorted events for well over 24 h at a time and is robust to power dropouts during battery changes as well as SD card replacement. A 24 h initial recording in a non-human primate M1 showed consistent spike shapes with the expected changes in neural activity during awake behaviour and sleep cycles. SIGNIFICANCE: The presented platform allows neural activity to be unobtrusively monitored and processed in real-time in freely behaving untethered animals-revealing insights that are not attainable through scheduled recording sessions. This system achieves the lowest power per channel to date and provides a robust, low-latency, low-bandwidth and verifiable outp

    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

    This paper investigates Continuous-Time (CT) signal acquisition as an activity-dependent and non-uniform sampling alternative to conventional fixed-rate digitisation. We demonstrate the applicability to biosignal representation by quantifying the achievable bandwidth saving by non-uniform quantisation to commonly recorded biological signal fragmentsallowing a compression ratio of 5 and 26 when applied to Electrocardiogram (ECG) and Extracellular Action Potential (EAP) signals respectively. We describe several desirable properties of CT sampling including bandwidth reduction, elimination/reduction of quantisation error and describe its impact on aliasing. This is followed by demonstration of a resource-efficient hardware implementation. We propose a novel circuit topology for a charge-based CT Analogue-to-Digital Converter (CT ADC) that has been optimised for the acquisition of neural signals. This has been implemented in a commercially-available 0.35µm CMOS technology occupying a compact footprint of 0.12mm². Silicon verified measurements demonstrate an 8-bit resolution and a 4kHz bandwidth with static power consumption of 3.75µWfrom a 1.5V supply. The dynamic power dissipation is completely activity-dependent, requiring 1.39pJ energy per conversion.

    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 Trans Biomed Circuits Syst, Vol: 12, Pages: 576-588

    Neuromodulation technologies are progressing from pacemaking and sensory operations to full closed-loop control. In particular, optogenetics-the genetic modification of light sensitivity into neural tissue allows for simultaneous optical stimulation and electronic recording. This paper presents a neural interface application-specified integrated circuit (ASIC) for intelligent optoelectronic probes. The architecture is designed to enable simultaneous optical neural stimulation and electronic recording. It provides four low noise (2.08  μV) recording channels optimized for recording local field potentials (LFPs) (0.1-300 Hz bandwidth, 5 mV range, sampled 10-bit@4 kHz), which are more stable for chronic applications. For stimulation, it provides six independently addressable optical driver circuits, which can provide both intensity (8-bit resolution across a 1.1 mA range) and pulse-width modulation for high-radiance light emitting diodes (LEDs). The system includes a fully digital interface using a serial peripheral interface (SPI) protocol to allow for use with embedded controllers. The SPI interface is embedded within a finite state machine (FSM), which implements a command interpreter that can send out LFP data whilst receiving instructions to control LED emission. The circuit has been implemented in a commercially available 0.35  μm CMOS technology occupying a 1.95 mm 1.10 mm footprint for mounting onto the head of a silicon probe. Measured results are given for a variety of bench-top, in vitro and in vivo experiments, quantifying system performance and also demonstrating concurrent recording and stimulation within relevant experimental models.

    Saiz Alia M, Askari A, Forte AE, Reichenbach JDTet al., 2018,

    A model of the human auditory brainstem response to running speech

    , ARO 2018
    Sherlock B, Warren SC, Alexandrov Y, Yu F, Stone J, Knight J, Neil MAA, Paterson C, French PMW, Dunsby Cet al., 2018,

    In vivo multiphoton microscopy using a handheld scanner with lateral and axial motion compensation

    , JOURNAL OF BIOPHOTONICS, Vol: 11, ISSN: 1864-063X
    Williams I, Leene L, Constandinou TG, 2018,

    Next Generation Neural Interface Electronics

    , Circuit Design Considerations for Implantable Devices, Editors: Cong, Publisher: River Publishers, Pages: 141-178, ISBN: 978-87-93519-86-2
    Angeles P, Tai Y, Pavese N, Vaidyanathan Ret al., 2017,

    Assessing Parkinson's disease motor symptoms using supervised learning algorithms

    , 21st International Congress of Parkinson's Disease and Movement Disorders, Publisher: WILEY, ISSN: 0885-3185
    Angeles P, Tai Y, Pavese N, Wilson S, Vaidyanathan Ret al., 2017,

    Automated assessment of symptom severity changes during Deep Brain Stimulation (DBS) therapy for Parkinson's disease

    , International Conference on Rehabilitation Robotics (ICORR), Publisher: IEEE, Pages: 1512-1517, ISSN: 1945-7898
    Bass C, Helkkula P, De Paola V, Clopath C, Bharath AAet al., 2017,

    Detection of axonal synapses in 3D two-photon images

    , PLOS ONE, Vol: 12, ISSN: 1932-6203
    Bishop CA, Newbould RD, Lee JSZ, Honeyfield L, Quest R, Colasanti A, Ali R, Mattoscio M, Cortese A, Nicholas R, Matthews PM, Muraro PA, Waldman ADet al., 2017,

    Analysis of ageing-associated grey matter volume in patients with multiple sclerosis shows excess atrophy in subcortical regions

    , NEUROIMAGE-CLINICAL, Vol: 13, Pages: 9-15, ISSN: 2213-1582
    Burridge JH, Lee ACW, Turk R, Stokes M, Whitall J, Vaidyanathan R, Clatworthy P, Hughes A-M, Meagher C, Franco E, Yardley Let al., 2017,

    Telehealth, Wearable Sensors, and the Internet: Will They Improve Stroke Outcomes Through Increased Intensity of Therapy, Motivation, and Adherence to Rehabilitation Programs?

    , JOURNAL OF NEUROLOGIC PHYSICAL THERAPY, Vol: 41, Pages: S32-S38, ISSN: 1557-0576
    Castronovo M, Mrachacz-Kersting N, Landi F, Jørgensen HR, Severinsen K, Farina Det al., 2017,

    Motor Unit Coherence at Low Frequencies Increases Together with Cortical Excitability Following a Brain-Computer Interface Intervention in Acute Stroke Patients

    , Pages: 1001-1005, ISSN: 2195-3562

    © Springer International Publishing AG 2017. This study aims at investigating the neurophysiological correlates of increased cortical excitability following a Brain-Computer interface based intervention in three acute stroke survivors. The analysis was performed on high-density EMG signals recorded from the Tibialis Anterior muscle. All patients showed an increased excitability in the motor cortex area of interest following the BCI intervention. Moreover, coherence between motor unit spike trains increased in the frequency band 1–5, Hz, suggesting an increase in the common oscillatory drive to the target muscle.

    Cayco-Gajic NA, Clopath C, Silver RA, 2017,

    Sparse synaptic connectivity is required for decorrelation and pattern separation in feedforward networks

    , NATURE COMMUNICATIONS, Vol: 8, ISSN: 2041-1723
    Caze RD, Jarvis S, Foust AJ, Schultz SRet al., 2017,

    Dendrites Enable a Robust Mechanism for Neuronal Stimulus Selectivity

    , NEURAL COMPUTATION, Vol: 29, Pages: 2511-2527, ISSN: 0899-7667
    Datta G, Violante IR, Scott G, Zimmerman K, Santos-Ribeiro A, Rabiner EA, Gunn RN, Malik O, Ciccarelli O, Nicholas R, Matthews PMet al., 2017,

    Translocator positron-emission tomography and magnetic resonance spectroscopic imaging of brain glial cell activation in multiple sclerosis

    , MULTIPLE SCLEROSIS JOURNAL, Vol: 23, Pages: 1469-1478, ISSN: 1352-4585
    Davila-Montero S, Barsakcioglu DY, Jackson A, Constandinou TG, Mason AJet al., 2017,

    Real-time Clustering Algorithm that Adapts to Dynamic Changes in Neural Recordings

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 690-693, ISSN: 0271-4302
    De Marcellis A, Palange E, Faccio M, Stanchieri GDP, Constandinou TGet al., 2017,

    A 250Mbps 24pJ/bit UWB-inspired Optical Communication System for Bioimplants

    , Turin, Italy, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Pages: 132-135
    Dreier JP, Fabricius M, Ayata C, Sakowitz OW, Shuttleworth CW, Dohmen C, Graf R, Vajkoczy P, Helbok R, Suzuki M, Schiefecker AJ, Major S, Winkler MKL, Kang E-J, Milakara D, Oliveira-Ferreira AI, Reiffurth C, Revankar GS, Sugimoto K, Dengler NF, Hecht N, Foreman B, Feyen B, Kondziella D, Friberg CK, Piilgaard H, Rosenthal ES, Westover MB, Maslarova A, Santos E, Hertle D, Sanchez-Porras R, Jewell SL, Balanca B, Platz J, Hinzman JM, Lueckl J, Schoknecht K, Schoell M, Drenckhahn C, Feuerstein D, Eriksen N, Horst V, Bretz JS, Jahnke P, Scheel M, Bohner G, Rostrup E, Pakkenberg B, Heinemann U, Claassen J, Carlson AP, Kowoll CM, Lublinsky S, Chassidim Y, Shelef I, Friedman A, Brinker G, Reiner M, Kirov SA, Andrew RD, Farkas E, Gueresir E, Vatter H, Chung LS, Brennan KC, Lieutaud T, Marinesco S, Maas AIR, Sahuquillo J, Dahlem MA, Richter F, Herreras O, Boutelle MG, Okonkwo DO, Bullock MR, Witte OW, Martus P, van den Maagdenberg AMJM, Ferrari MD, Dijkhuizen RM, Shutter LA, Andaluz N, Schulte AP, MacVicar B, Watanabe T, Woitzik J, Lauritzen M, Strong AJ, Hartings JAet al., 2017,

    Recording, analysis, and interpretation of spreading depolarizations in neurointensive care: Review and recommendations of the COSBID research group

    , JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM, Vol: 37, Pages: 1595-1625, ISSN: 0271-678X
    Farina D, Castronovo AM, Vujaklija I, Sturma A, Salminger S, Hofer C, Aszmann Oet al., 2017,

    Common Synaptic Input to Motor Neurons and Neural Drive to Targeted Reinnervated Muscles

    , JOURNAL OF NEUROSCIENCE, Vol: 37, Pages: 11285-11292, ISSN: 0270-6474
    Feng P, Constandinou TG, Yeon P, Ghovanloo Met al., 2017,

    Millimeter-Scale Integrated and Wirewound Coils for Powering Implantable Neural Microsystems

    , IEEE Biomedical Circuits and Systems (BioCAS) Conference, Pages: 488-491
    Forte AE, Etard O, Reichenbach J, 2017,

    Complex Auditory-brainstem Response to the Fundamental Frequency of Continuous Natural Speech

    , ARO 2017
    Forte AE, Etard O, Reichenbach J, 2017,

    Selective auditory attention modulates the human brainstem's response to running speech

    , Basic Auditory Science 2017
    Forte AE, Etard O, Reichenbach T, 2017,

    The human auditory brainstem response to running speech reveals a subcortical mechanism for selective attention

    , ELIFE, Vol: 6, ISSN: 2050-084X
    Gao C, Ghoreishizadeh S, Liu Y, Constandinou Tet al., 2017,

    On-chip ID Generation for Multi-node Implantable Devices using SA-PUF

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 678-681, ISSN: 0271-4302
    Ghajari M, Hellyer PJ, Sharp DJ, 2017,

    Computational modelling of traumatic brain injury predicts the location of chronic traumatic encephalopathy pathology

    , BRAIN, Vol: 140, Pages: 333-343, ISSN: 0006-8950
    Ghoreishizadeh SS, Haci D, Liu Y, Constandinou TGet al., 2017,

    A 4-Wire Interface SoC for Shared Multi- Implant Power Transfer and Full-duplex Communication

    , 8th IEEE Latin American Symposium on Circuits & Systems (LASCAS), Publisher: IEEE

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