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    Dehkhoda F, Soltan A, Ramezani R, Zhao H, Liu Y, Constandinou T, Degenaar Pet al., 2015,

    Smart Optrode for Neural Stimulation and Sensing

    , 2015 IEEE SENSORS, Publisher: IEEE, Pages: 1965-1968, ISSN: 1930-0395
    Faliagkas K, Leene LB, Constandinou TG, 2015,

    A Novel Neural Recording System Utilising Continuous Time Energy Based Compression

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 3000-3003, ISSN: 0271-4302
    Jackson A, Constandinou TG, Eftekhar A, Quiroga RQ, Navajas JAet al., 2015,

    System for a Brain-Computer Interface

    Lauteslager T, Nicolaou N, Lande TS, Constandinou Tet al., 2015,

    Functional neuroimaging using UWB impulse radar: A feasibility study

    , 11th IEEE Annual Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 406-409, ISSN: 2163-4025
    Rapeaux A, Nikolic K, Williams I, Eftekhar A, Constandinou TGet al., 2015,

    Fiber Size-Selective Stimulation using Action Potential Filtering for a Peripheral Nerve Interface: A Simulation Study

    , 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, Pages: 3411-3414, ISSN: 1557-170X
    Reed S, Georgiou P, Constandinou TG, 2015,

    Method and Apparatus for Sensing a Property of a Fluid

    , 8,986,525 B2

    A device for sensing a property of a fluid comprising a first substrate having formed thereon a sensor configured in use to come into contact with a fluid in order to sense a property of the fluid, and a wireless transmitter for transmitting data over a wireless data link and a second substrate having formed thereon a wireless receiver for receiving data transmitted over said wireless link by said wireless transmitter. The first substrate is fixed to or within said second substrate. Additionally or alternatively, the device comprises a first substrate defining one or more microfluidic structures for receiving a fluid to be sensed and a second substrate comprising or having attached thereto a multiplicity of fluid sensors, the number of sensors being greater than the number of microfluidic structures. The second substrate is in contact with the first substrate such that at least one of the sensors is aligned with the or each microfluidic structure so as to provide an active sensor for the or each structure, and such that one or more of the sensors is or are not aligned with any microfluidic structure and is or are thereby redundant.

    Williams I, Luan S, Jackson A, Constandinou TGet al., 2015,

    A Scalable 32 Channel Neural Recording and Real-time FPGA Based Spike Sorting System

    , 11th IEEE Annual Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 188-191, ISSN: 2163-4025
    Woods S, Constandinou TG, 2015,

    Engineering Micro Mechanical Systems for the Next Generation Wireless Capsule Endoscopy

    , BioMed Research International, Vol: 2015, ISSN: 2314-6141

    Wireless capsule endoscopy (WCE) enables the detection and diagnosis of in ammatory bowel diseases such as Crohn's disease and ulcerative colitis. However treatment of these pathologies through the administering of therapy can only be achieved through conventional means. This paper describes the next generation wireless capsule endoscopy which has increased functionality to allow for targeted drug delivery in the small intestinal tract. A prototype microrobot fabricated in Nylon 6 is presented which is capable of resisting natural peristaltic pressure through the deployment of an integrated holding mechanism and delivering targeted therapy. The holding action is achieved by extending an \anchor" spanning an effective 60.4mm circumference, for a 11.0mm diameterWCE. This function is achieved by a mechanism that occupies only 347.0mm3 volume, including mechanics and actuator. A micro-positioning mechanism is described which utilises a single micromotor to radially position then deploy a needle 1.5mm outside the microrobot's body for the purpose of delivering a 1 ml dose of medication to a targeted site. An analysis of the mechanics required to drive the holding mechanism is presented and an overview of micro-actuators and the state of the art in WCE is discussed. The novel mechanisms offer increased functionality to WCE. It is envisaged that this increased ability to perform targeted therapy will empower the next generation of WCE to help diagnose and treat pathologies of the GI tract.

    Woods SP, Constandinou TG, 2015,

    A Novel Holding Mechanism for Next Generation Active Wireless Capsule Endoscopy

    , 37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 1181-1185, ISSN: 1557-170X
    Zhao H, Dehkhoda F, Ramezani R, Sokolov D, Degenaar P, Liu Y, Constandinou Tet al., 2015,

    A CMOS-based Neural Implantable Optrode for Optogenetic Stimulation and Electrical Recording

    , 11th IEEE Annual Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 286-289, ISSN: 2163-4025
    Barsakcioglu D, Liu Y, Bhunjun P, Navajas J, Eftekhar A, Jackson A, Quian Quiroga R, Constandinou TGet al., 2014,

    An Analogue Front-End Model for Developing Neural Spike Sorting Systems

    , IEEE Transactions on Biomedical Circuits and Systems, Vol: 8, Pages: 216-227
    Demarchou E, Georgiou J, Nicolaou N, Constandinou Tet al., 2014,

    Anesthetic-induced changes in EEG activity: a graph theoretical approach

    , IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 45-48, ISSN: 2163-4025
    Eftekhar A, Juffali W, El-Imad J, Constandinou TG, Toumazou Cet al., 2014,

    Ngram-Derived Pattern Recognition for the Detection and Prediction of Epileptic Seizures

    , PLOS ONE, Vol: 9, ISSN: 1932-6203
    Guven O, Eftekhar A, Hoshyar R, Frattini G, Kindt W, Constandinou TGet al., 2014,

    Realtime ECG Baseline Removal: An Isoelectric Point Estimation Approach

    , IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 29-32, ISSN: 2163-4025
    Leene LB, Constandinou TG, 2014,

    Ultra-low power design strategy for two-stage amplifier topologies

    , ELECTRONICS LETTERS, Vol: 50, Pages: 583-584, ISSN: 0013-5194
    Luan S, Constandinou TG, 2014,

    A charge-metering method for voltage-mode neural stimulation

    , JOURNAL OF NEUROSCIENCE METHODS, Vol: 224, Pages: 39-47, ISSN: 0165-0270
    Luan S, Williams I, Nikolic K, Constandinou TGet al., 2014,

    Neuromodulation: present and emerging methods.

    , Front Neuroeng, Vol: 7, ISSN: 1662-6443

    Neuromodulation has wide ranging potential applications in replacing impaired neural function (prosthetics), as a novel form of medical treatment (therapy), and as a tool for investigating neurons and neural function (research). Voltage and current controlled electrical neural stimulation (ENS) are methods that have already been widely applied in both neuroscience and clinical practice for neuroprosthetics. However, there are numerous alternative methods of stimulating or inhibiting neurons. This paper reviews the state-of-the-art in ENS as well as alternative neuromodulation techniques-presenting the operational concepts, technical implementation and limitations-in order to inform system design choices.

    Navajas J, Barsakcioglu D, Eftekhar A, Jackson A, Constandinou TG, Quian Quiroga Ret al., 2014,

    Minimum Requirements for Accurate and Efficient Real-Time On-Chip Spike Sorting

    , Journal of Neuroscience Methods, Pages: 51-64
    Paraskevopoulou SE, Wu D, Eftekhar A, Constandinou TGet al., 2014,

    Hierarchical Adaptive Means (HAM) Clustering for Hardware-Efficient, Unsupervised and Real-time Spike Sorting.

    , Journal of Neuroscience Methods, Vol: 235, Pages: 145-156, ISSN: 1872-678X

    This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation.

    Reverter F, Prodromakis T, Liu Y, Georgiou P, Nikolic K, Constandinou TGet al., 2014,

    Design Considerations for a CMOS Lab-on-Chip Microheater Array to Facilitate the in vitro Thermal Stimulation of Neurons

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 630-633

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