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    Liu Y, L Pereira J, Constandinou T, 2017,

    Event-driven processing for hardware-efficient neural spike sorting.

    , J Neural Eng

    The prospect of real-time and on-node spike sorting provides a genuine opportunity to push the envelope for large-scale integration of neural recording systems. In such systems the hardware resource, power requirements and data bandwidth increase linearly with channel count. Event-based (or data-driven) processing can here provide a new efficient means for hardware implementation that is completely activity dependant. In this work, we investigate using continuous time level-crossing sampling for efficient data representation and subsequent spike processing. We first compare signals (using synthetic neural datasets) that are encoded using this technique against conventional sampling. It is observed that considerably lower data rates are achievable when utilising 7 bits or less to represent the signals, whilst maintaining the signal fidelity. We then show how such a representation can be directly exploited by extracting simple time domain features from the bitstream to perform neural spike sorting. The proposed method is implemented in a low power FPGA platform to demonstrate the hardware viability. Results obtained using both MATLAB and reconfigurable logic (FPGA) hardware indicate that feature extraction and spike sorting accuracies can be achieved with comparable or better accuracy than reference methods whilst also requiring relatively low hardware cost.

    Luan S, Williams I, De-Carvalho F, Grand L, Jackson A, Quian Quiroga R, Constandinou TGet al., 2017,

    Standalone headstage for neural recording with real-time spike sorting and data logging

    , BNA Festival of Neuroscience, Publisher: The British Neuroscience Association Ltd
    Mace M, Rinne P, Kinany N, Bentley P, Burdet Eet al., 2017,

    Collaborative Gaming to Enhance Patient Performance During Virtual Therapy

    , 3rd International Conference on NeuroRehabilitation (ICNR), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 375-379, ISSN: 2195-3562
    Maslik M, Liu Y, Lande TS, Constandinou TGet al., 2017,

    A charge-based ultra-low power continuous-time ADC for data driven neural spike processing

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 1420-1423

    The paper presents a novel topology of a continuous-time analogue-to-digital converter (CT-ADC) featuring ultra-low static power consumption, activity-dependent dynamic consumption, and a compact footprint. This is achieved by utilising a novel charge-packet based threshold generation method, that alleviates the requirement for a conventional feedback DAC. The circuit has a static power consumption of 3.75uW, with dynamic energy of 1.39pJ/conversion level. This type of converter is thus particularly well-suited for biosignals that are generally sparse in nature. The circuit has been optimised for neural spike recording by capturing a 3kHz bandwidth with 8-bit resolution. For a typical extracellular neural recording the average power consumption is in the order of ~4uW. The circuit has been implemented in a commercially available 0.35um CMOS technology with core occupying a footprint of 0.12

    Mifsud A, Haci D, Ghoreishizadeh S, Liu Y, Constandinou TGet al., 2017,

    Adaptive Power Regulation and Data Delivery for Multi-Module Implants

    , IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 1-4
    Monti RP, Lorenz R, Braga RM, Anagnostopoulos C, Leech R, Montana Get al., 2017,

    Real-time estimation of dynamic functional connectivity networks

    , HUMAN BRAIN MAPPING, Vol: 38, Pages: 202-220, ISSN: 1065-9471
    Quicke P, Barnes SJ, Knöpfel T, 2017,

    Imaging of Brain Slices with a Genetically Encoded Voltage Indicator.

    , Methods Mol Biol, Vol: 1563, Pages: 73-84

    Functional fluorescence microscopy of brain slices using voltage sensitive fluorescent proteins (VSFPs) allows large scale electrophysiological monitoring of neuronal excitation and inhibition. We describe the equipment and techniques needed to successfully record functional responses optical voltage signals from cells expressing a voltage indicator such as VSFP Butterfly 1.2. We also discuss the advantages of voltage imaging and the challenges it presents.

    Quicke P, Neil M, Knopfel T, Schultz SR, Foust AJet al., 2017,

    Source-Localized Multifocal Two-Photon Microscopy for High-Speed Functional Imaging

    , 71st Annual Meeting of the Society-of-General-Physiologists (SGP) on Optical Revolution in Physiology - From Membrane to Brain, Publisher: ROCKEFELLER UNIV PRESS, Pages: 13A-14A, ISSN: 0022-1295
    Roberts RE, Ahmad H, Arshad Q, Patel M, Dima D, Leech R, Seemungal BM, Sharp DJ, Bronstein AMet al., 2017,

    Functional neuroimaging of visuo-vestibular interaction

    , BRAIN STRUCTURE & FUNCTION, Vol: 222, Pages: 2329-2343, ISSN: 1863-2653
    Rogers ML, Leong CL, Gowers SAN, Samper IC, Jewell SL, Khan A, McCarthy L, Pahl C, Tolias CM, Walsh DC, Strong AJ, Boutelle MGet al., 2017,

    Simultaneous monitoring of potassium, glucose and lactate during spreading depolarization in the injured human brain - Proof of principle of a novel real-time neurochemical analysis system, continuous online microdialysis

    , JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM, Vol: 37, Pages: 1883-1895, ISSN: 0271-678X
    Schultz SR, Copeland CS, Foust AJ, Quicke P, Schuck Ret al., 2017,

    Advances in Two-Photon Scanning and Scanless Microscopy Technologies for Functional Neural Circuit Imaging

    , PROCEEDINGS OF THE IEEE, Vol: 105, Pages: 139-157, ISSN: 0018-9219
    Sherlock B, Warren SC, Alexandrov Y, Yu F, Stone J, Knight J, Neil MAA, Paterson C, French PMW, Dunsby Cet al., 2017,

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

    , J Biophotonics

    This paper reports a handheld multiphoton fluorescence microscope designed for clinical imaging that incorporates axial motion compensation and lateral image stabilization. Spectral domain optical coherence tomography is employed to track the axial position of the skin surface, and lateral motion compensation is realised by imaging the speckle pattern arising from the optical coherence tomography beam illuminating the sample. Our system is able to correct lateral sample velocities of up to ~65 μm s(-1) . Combined with the use of negative curvature microstructured optical fibre to deliver tunable ultrafast radiation to the handheld multiphoton scanner without the need of a dispersion compensation unit, this instrument has potential for a range of clinical applications. The system is used to compensate for both lateral and axial motion of the sample when imaging human skin in vivo.

    Sweeney Y, Clopath C, 2017,

    Emergent spatial synaptic structure from diffusive plasticity

    , EUROPEAN JOURNAL OF NEUROSCIENCE, Vol: 45, Pages: 1057-1067, ISSN: 0953-816X
    Szostak K, Mazza F, Maslik M, Feng P, Leene L, Constandinou TGet al., 2017,

    Microwire-CMOS Integration of mm-Scale Neural Probes for Chronic Local Field Potential Recording

    , IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 1-4
    Troiani F, Nikolic K, Constandinou TG, 2017,

    Optical coherence tomography for compound action potential detection: a computational study

    , SPIE/OSA European Conferences on Biomedical Optics (ECBO), Pages: 1-3

    The feasibility of using time domain optical coherence tomography (TD-OCT)to detect compound action potential in a peripheral nerve and the setup characteristics, are studied through the use of finite-difference time-domain (FDTD) technique.

    Xiloyannis M, Gavriel C, Thomik AAC, Faisal AAet al., 2017,

    Gaussian Process Autoregression for Simultaneous Proportional Multi-Modal Prosthetic Control with Natural Hand Kinematics

    , IEEE Transactions on Neural Systems and Rehabilitation Engineering, ISSN: 1534-4320

    OAPA Matching the dexterity, versatility and robustness of the human hand is still an unachieved goal in bionics, robotics and neural engineering. A major limitation for hand prosthetics lies in the challenges of reliably decoding user intention from muscle signals when controlling complex robotic hands. Most of the commercially available prosthetic hands use musclerelated signals to decode a finite number of predefined motions and some offer proportional control of open/close movements of the whole hand. Here, in contrast, we aim to offer users flexible control of individual joints of their artificial hand. We propose a novel framework for decoding neural information that enables a user to independently control 11 joints of the hand in a continuous manner - much like we control our natural hands. Towards this end, we instructed 6 able-bodied subjects to perform everyday object manipulation tasks combining both dynamic, free movements (e.g. grasping) and isometric force tasks (e.g. squeezing). We recorded the electromyographic (EMG) and mechanomyographic (MMG) activities of 5 extrinsic muscles of the hand in the forearm, while simultaneously monitoring 11 joints of hand and fingers using a sensorised data glove that tracked the joints of the hand. Instead of learning just a direct mapping from current muscle activity to intended hand movement, we formulated a novel autoregressive approach that combines the context of previous hand movements with instantaneous muscle activity to predict future hand movements. Specifically, we evaluated a linear Vector AutoRegressive Moving Average model with Exogenous inputs (VARMAX) and a novel Gaussian Process (GP) autoregressive framework to learn the continuous mapping from hand joint dynamics and muscle activity to decode intended hand movement. Our GP approach achieves high levels of performance (RMSE of 8/s and = 0:79). Crucially, we use a small set of sensors that allows us to control a larger set of independently actuated de

    Yao L, Sheng X, Zhang D, Jiang N, Farina D, Zhu Xet al., 2017,

    A BCI System Based on Somatosensory Attentional Orientation

    , IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol: 25, Pages: 78-87, ISSN: 1534-4320

    © 2001-2011 IEEE. We propose and test a novel brain-computer interface (BCI) based on imagined tactile sensation. During an imagined tactile sensation, referred to as somatosensory attentional orientation (SAO), the subject shifts and maintains somatosensory attention on a body part, e.g., left or right hand. The SAO can be detected from EEG recordings for establishing a communication channel. To test for the hypothesis that SAO on different body parts can be discriminated from EEG, 14 subjects were assigned to a group who received an actual sensory stimulation (STE-Group), and 18 subjects were assigned to the SAO only group (SAO-Group). In single trials, the STE-Group received tactile stimulation first (both wrists simultaneously stimulated), and then maintained the attention on the selected body part (without stimulation). The same group also performed the SAO task first and then received the tactile stimulation. Conversely, the SAO-Group performed SAO without any stimulation, neither before nor after the SAO. In both the STE-Group and SAO-Group, it was possible to identify the SAO-related oscillatory activation that corresponded to a contralateral event-related desynchronization (ERD) stronger than the ipsilateral ERD. Discriminative information, represented as R 2 , was found mainly on the somatosensory area of the cortex. In the STE-Group, the average classification accuracy of SAO was 83.6%, and it was comparable with tactile BCI based on selective sensation (paired-T test, $P > 0.05$ ). In the SAO-Group the average online performance was 75.7%. For this group, after frequency band selection the offline perfor mance reached 82.5% on average, with ≥ 80% for 12 subjects and ≥ 95% for four subjects. Complementary to tactile sensation, the SAO does not require sensory stimulation, with the advantage of being completely independent from the stimulus.

    Angeles P, Mace M, Admiraal M, Burdet E, Pavese N, Vaidyanathan Ret al., 2016,

    A Wearable Automated System to Quantify Parkinsonian Symptoms Enabling Closed Loop Deep Brain Stimulation

    , 17th Annual Conference on Towards Autonomous Robotic Systems (TAROS), Publisher: SPRINGER INT PUBLISHING AG, Pages: 8-19, ISSN: 0302-9743
    Antic SD, Empson RM, Knoepfel T, 2016,

    Voltage imaging to understand connections and functions of neuronal circuits

    , JOURNAL OF NEUROPHYSIOLOGY, Vol: 116, Pages: 135-152, ISSN: 0022-3077
    Arulkumaran K, Dilokthanakul N, Shanahan M, Bharath AAet al., 2016,

    Classifying Options for Deep Reinforcement Learning.

    Aszmann OC, Vujaklija I, Roche AD, Salminger S, Herceg M, Sturma A, Hruby LA, Pittermann A, Hofer C, Amsuess S, Farina Det al., 2016,

    Elective amputation and bionic substitution restore functional hand use after critical soft tissue injuries

    , SCIENTIFIC REPORTS, Vol: 6, ISSN: 2045-2322
    Badura A, Clopath C, Schonewille M, De Zeeuw CIet al., 2016,

    Modeled changes of cerebellar activity in mutant mice are predictive of their learning impairments

    , SCIENTIFIC REPORTS, Vol: 6, ISSN: 2045-2322
    Barsakcioglu DY, Constandinou TG, 2016,

    A 32-Channel MCU-based Feature Extraction and Classification for Scalable On-node Spike Sorting

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 1310-1313, ISSN: 0271-4302
    Berditchevskaia A, Caze RD, Schultz SR, 2016,

    Performance in a GO/NOGO perceptual task reflects a balance between impulsive and instrumental components of behaviour

    , SCIENTIFIC REPORTS, Vol: 6, ISSN: 2045-2322
    Bevan R, Griffiths L, Watkins L, Evans R, McInerney B, Rees M, Calabrese M, Magliozzi R, Reynolds R, Allen I, Fitzgerald D, Howell Oet al., 2016,

    Significant meningeal inflammation and cortical neurodegeneration in a post-mortem cohort of short disease duration multiple sclerosis

    , 32nd Congress of the European-Committee-for-Treatment-and-Research-in-Multiple-Sclerosis (ECTRIMS), Publisher: SAGE PUBLICATIONS LTD, Pages: 468-468, ISSN: 1352-4585
    Braga RM, Fu RZ, Seemungal BM, Wise RJS, Leech Ret al., 2016,

    Eye Movements during Auditory Attention Predict Individual Differences in Dorsal Attention Network Activity

    Chen S, Augustine GJ, Chadderton P, 2016,

    The cerebellum linearly encodes whisker position during voluntary movement

    , ELIFE, Vol: 5, ISSN: 2050-084X
    Cheung K, Schultz SR, Luk W, 2016,

    NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors

    De Guio F, Jouvent E, Biessels GJ, Black SE, Brayne C, Chen C, Cordonnier C, De Leeuw F-E, Dichgans M, Doubal F, Duering M, Dufouil C, Duzel E, Fazekas F, Hachinski V, Ikram MA, Linn J, Matthews PM, Mazoyer B, Mok V, Norrving B, O'Brien JT, Pantoni L, Ropele S, Sachdev P, Schmidt R, Seshadri S, Smith EE, Sposato LA, Stephan B, Swartz RH, Tzourio C, van Buchem M, van der Lugt A, van Oostenbrugge R, Vernooij MW, Viswanathan A, Werring D, Wollenweber F, Wardlaw JM, Chabriat Het al., 2016,

    Reproducibility and variability of quantitative magnetic resonance imaging markers in cerebral small vessel disease

    , JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM, Vol: 36, Pages: 1319-1337, ISSN: 0271-678X
    De Marcellis A, Palange E, Faccio M, Nubile L, Stanchieri GDP, Petrucci S, Constandinou Tet al., 2016,

    A New Optical UWB Modulation Technique for 250Mbps Wireless Link in Implantable Biotelemetry Systems

    , 30th Eurosensors Conference, Publisher: ELSEVIER SCIENCE BV, Pages: 1676-1680, ISSN: 1877-7058

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