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  • Conference paper
    Hassan S, Nightingale AM, Leong CL, Zhang Y, Evans G, Boutelle MG, Niu XZet al., 2016,

    Wearable droplet-based microfluidic sensor device for continuous sampling and real-time analysis

    , Pages: 43-44

    This paper reports a wearable droplet-microfluidic based continuous chemical sensing device. The device combines microdialysis and droplet microfluidic techniques, can continuously sample from interstitial or other body fluids into nanolitre droplets and perform biochemical assays and measurements in situ and in real-time.

  • Journal article
    Casale AE, Foust AJ, Bal T, McCormick DAet al., 2015,

    Cortical Interneuron Subtypes Vary in Their Axonal Action Potential Properties

    , JOURNAL OF NEUROSCIENCE, Vol: 35, Pages: 15555-15567, ISSN: 0270-6474
  • Journal article
    Jarvis S, Schultz SR, 2015,

    Prospects for optogenetic augmentation of brain function

    , Frontiers in Systems Neuroscience, Vol: 9, ISSN: 1663-3563

    The ability to optically control neural activity opens up possibilities for the restoration of normal function following neurological disorders. The temporal precision, spatial resolution and neuronal specificity that optogenetics offers is unequalled by other available methods, so will it be suitable for not only restoring but also extending brain function? As the first demonstrations of optically ``implanted'' novel memories emerge, we examine the suitability of optogenetics as a technique for extending neural function. While optogenetics is an effective tool for altering neural activity, the largest impediment for optogenetics in neural augmentation is our systems level understanding of brain function. Furthermore, a number of clinical limitations currently remain as substantial hurdles for the applications proposed. While neurotechnologies for treating brain disorders and interfacing with prosthetics have advanced rapidly in the past few years, partially addressing some of these critical problems, optogenetics is not yet suitable for use in humans. Instead we conclude that for the immediate future, optogenetics is the neurological equivalent of the 3D printer: its flexibility providing an ideal tool for testing and prototyping solutions for treating brain disorders and augmenting brain function.

  • Conference paper
    Dehkhoda F, Soltan A, Ramezani R, Zhao H, Liu Y, Constandinou TG, Degenaar Pet al., 2015,

    Smart Optrode for Neural Stimulation and Sensing

    , 2015 IEEE Sensors Conference, Publisher: IEEE, Pages: 1-4

    Implantable neuro-prosthetics considerable clinical benefit to a range of neurological conditions. Optogenetics is a particular recent interest which utilizes high radiance light for photo-activation of genetic cells. This can provide improved biocompatibility and neural targeting over electrical stimuli. To date the primary optical delivery method in tissue for optogenetics has been via optic fibre which makes large scale multiplexing difficult. An alternative approach is to incorporate optical micro-emitters directly on implantable probes but this still requires electrical multiplexing. In this work, we demonstrate a fully active optoelectronic probe utilizing industry standard 0.35μm CMOS technology, capable of both light delivery and electrical recording. The incorporation of electronic circuits onto the device further allows us to incorporate smart sensors to determine diagnostic state to explore long term viability during chronic implantation.

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

    A scalable 32 channel neural recording and real-time FPGA based spike sorting system

    , IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 187-191

    This demo presents a scalable a 32-channel neuralrecording platform with real-time, on-node spike sorting ca-pability. The hardware consists of: an Intan RHD2132 neuralamplifier; a low power Igloo ® nano FPGA; and an FX3 USB3.0 controller. Graphical User Interfaces for controlling thesystem, displaying real-time data, and template generation witha modified form of WaveClus are demonstrated.

  • Journal article
    Mamun KA, Mace M, Lutman ME, Stein J, Liu X, Aziz T, Vaidyanathan R, Wang Set al., 2015,

    Movement decoding using neural synchronisation and inter-hemispheric connectivity from deep brain local field potentials

    , Journal of Neural Engineering, Vol: 12, ISSN: 1741-2560

    Objective. Correlating electrical activity within the human brain to movement is essential for developing and refining interventions (e.g. deep brain stimulation (DBS)) to treat central nervous system disorders. It also serves as a basis for next generation brain–machine interfaces (BMIs). This study highlights a new decoding strategy for capturing movement and its corresponding laterality from deep brain local field potentials (LFPs). Approach. LFPs were recorded with surgically implanted electrodes from the subthalamic nucleus or globus pallidus interna in twelve patients with Parkinson's disease or dystonia during a visually cued finger-clicking task. We introduce a method to extract frequency dependent neural synchronization and inter-hemispheric connectivity features based upon wavelet packet transform (WPT) and Granger causality approaches. A novel weighted sequential feature selection algorithm has been developed to select optimal feature subsets through a feature contribution measure. This is particularly useful when faced with limited trials of high dimensionality data as it enables estimation of feature importance during the decoding process. Main results. This novel approach was able to accurately and informatively decode movement related behaviours from the recorded LFP activity. An average accuracy of 99.8% was achieved for movement identification, whilst subsequent laterality classification was 81.5%. Feature contribution analysis highlighted stronger contralateral causal driving between the basal ganglia hemispheres compared to ipsilateral driving, with causality measures considerably improving laterality discrimination. Significance. These findings demonstrate optimally selected neural synchronization alongside causality measures related to inter-hemispheric connectivity can provide an effective control signal for augmenting adaptive BMIs. In the case of DBS patients, acquiring such signals requires no additional surgery whilst providing a rela

  • Conference paper
    Schuck R, Quicke P, Hwang JK, Annecchino L, Schultz SRet al., 2015,

    Rapid three dimensional two photon neural population scanning

    , 37th Annual International IEEE EMBS Conference of the IEEE Engineering in Medicine and Biology Society, Publisher: IEEE, Pages: 5867-5870, ISSN: 1557-170X

    Recording the activity of neural populationsat high sampling rates is a fundamental requirement forunderstanding computation in neural circuits. Two photonmicroscopy provides one promising approach towards this.However, neural circuits are three dimensional, and functionalimaging in two dimensions fails to capture the 3D natureof neural dynamics. Electrically tunable lenses (ETLs) providea simple and cheap method to extend laser scanningmicroscopy into the relatively unexploited third dimension.We have therefore incorporated them into our Adaptive SpiralScanning (SSA) algorithm, which calculates kinematicallyefficient scanning strategies using radially modulated spiralpaths. We characterised the response of the ETL, incorporatedits dynamics using MATLAB models of the SSA algorithmand tested the models on populations of Izhikevich neuronsof varying size and density. From this, we show that ouralgorithms can theoretically at least achieve sampling rates of36.2Hz compared to 21.6Hz previously reported for 3D scanningtechniques.

  • Conference paper
    Tolkiehn M, Schultz, 2015,

    Multi-Unit Activity contains information about spatial stimulus structure in mouse primary visual cortex

    , 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Publisher: IEEE, Pages: 3771-3774, ISSN: 1557-170X

    This study investigates the spatial and directionaltuning of Multi-Unit Activity (MUA) in mouse primary visualcortex and how MUA can reflect spatiotemporal structurescontained in moving gratings. Analysis of multi-shank laminarelectrophysiological recordings from mouse primary visualcortex indicates a directional preference for moving gratingsaround 180 , while preferred spatial frequency peaks around0.02 cycles per degree, which is similar as reported in single-unitstudies. Using only features from MUA, we further achieved asignificant performance in decoding spatial frequency or directionof moving gratings, with average decoding performancesof up to 58.54% for 8 directions, and 44% correctly identifiedspatial frequencies against chance level of 16.7%.

  • Conference paper
    Hallett E, Woodward R, Schultz SR, Vaidyanathan Ret al., 2015,

    Rapid bicycle gear switching based on physiological cues

    , IEEE CASE 2015, Publisher: IEEE, Pages: 377-382

    This paper discusses the merits of Mechanomyography (MMG) sensors in capturing and isolating muscle activity in high interference environs, with application to `hands free' gear shifting on a bicycle for users with limited extremity movement. MMG (acoustic) muscle sensing provides a simple and rugged alternative to physiological sensing for machine interface in the field, but suffers from interfering artifacts (in particular motion) which has limited its mainstream use. We introduce a system fusing MMG with a filter based on Inertial Measurement (IMU) to isolate muscle activity in the presence of interfering motion and vibrations. The system identifies user-initiated muscle trigger profiles during laboratory testing, allowing parameterization of MMG and IMU signals to identify purposeful muscle contractions (triggers) and to omit false triggers resulting from cycle/road vibration or rider movement. During laboratory testing the success rate of trigger identification was 88.5% while cycling with an average of 0.87 false triggers /min. During road testing the success rate was 72.5% and false triggers were more frequent at 3.7 /min. These results hold strong promise for alternative triggering mechanisms to the standard bar-end shifters used in current off-the-shelf cycling group sets, enabling amputees or people of reduced arm or hand dexterity to change gears while riding. Further testing will explore the use of signal filters on MMG data and further use of IMU data as feedback to increase false triggers rejection. Wider applications include a broad range of machine-interaction research.

  • Conference paper
    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, Pages: 3411-3414

    Functional electrical stimulation is a powerfultool for restoration of function after nerve injury. Howeverselectivity of stimulation remains an issue. This paper presentsan alternative stimulation technique to obtain fiber size-selectivestimulation of nerves using FDA-approved electrode implants.The technique was simulated for the ventral roots ofXenopus Laevis, motivated by an application in bladder control. Thetechnique relies on applying a high frequency alternatingcurrent to filter out action potentials in larger fibers, resultingin selective stimulation of the smaller fibers. Results predict thatthe technique can distinguish fibers with only a 2 µm differencein diameter (for nerves not exceeding 2 mm in diameter). Thestudy investigates the behaviour of electrically blocked nervesin detail. Model imperfections and simplifications yielded someartefacts in the results, as well as unexpected nerve behaviourwhich is tentatively explained.

  • Journal article
    Sadeh S, Clopath C, Rotter S, 2015,

    Processing of Feature Selectivity in Cortical Networks with Specific Connectivity (vol 10, e0127547, 2015)

    , PLOS ONE, Vol: 10, ISSN: 1932-6203
  • Journal article
    Reichenbach JDT, Meltzer B, Reichenbach CS, Braiman C, Schiff ND, Hudspeth AJet al., 2015,

    The steady-state response of the cerebral cortex to the beat of music reflects both the comprehension of music and attention

    , Frontiers in Human Neuroscience, Vol: 9, ISSN: 1662-5161

    The brain's analyses of speech and music share a range of neural resources and mechanisms. Music displays a temporal structure of complexity similar to that of speech, unfolds over comparable timescales, and elicits cognitive demands in tasks involving comprehension and attention. During speech processing, synchronized neural activity of the cerebral cortex in the delta and theta frequency bands tracks the envelope of a speech signal, and this neural activity is modulated by high-level cortical functions such as speech comprehension and attention. It remains unclear, however, whether the cortex also responds to the natural rhythmic structure of music and how the response, if present, is influenced by higher cognitive processes. Here we employ electroencephalography (EEG) to show that the cortex responds to the beat of music and that this steady-state response reflects musical comprehension and attention. We show that the cortical response to the beat is weaker when subjects listen to a familiar tune than when they listen to an unfamiliar, nonsensical musical piece. Furthermore, we show that in a task of intermodal attention there is a larger neural response at the beat frequency when subjects attend to a musical stimulus than when they ignore the auditory signal and instead focus on a visual one. Our findings may be applied in clinical assessments of auditory processing and music cognition as well as in the construction of auditory brain-machine interfaces.

  • Conference paper
    Yu X, Zecharia A, Zhang Z, Yang Q, Yustos R, Jager P, Vyssotski AL, Maywood ES, Chesham JE, Ma Y, Brickley SG, Hastings MH, Franks NP, Wisden Wet al., 2015,

    A LOCAL CIRCADIAN CLOCK IN HISTAMINERGIC NEURONS INFLUENCES SLEEP ARCHITECTURE

    , 44th Annual Meeting of the European-Histamine-Research-Society (EHRS), Publisher: SPRINGER BASEL AG, Pages: S20-S20, ISSN: 1023-3830
  • Conference paper
    Faliagkas K, Leene L, Constandinou TG, 2015,

    A Novel Neural Recording System Utilising Continuous Time Energy Based Compression

    , IEEE International Symposium on Circuits & Systems (ISCAS), Publisher: IEEE, Pages: 3000-3003

    This work presents a new data compression methodthat uses an energy operator to exploit the correlated energy inneural recording features in order to achieve adaptive sampling.This approach enhances conventional data converter topologieswith the power saving of asynchronous systems while maintaininglow complexity & high efficiency. The proposed scheme enablesthe transmission of 0:7kS/s, while preserving the features of thesignal with an accuracy of 95%. It is also shown that the operationof the system is not susceptible to noise, even for signals with 1dBSNR. The whole system consumes 3:94mWwith an estimated areaof 0:093mm2.

  • Journal article
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Appearance-based indoor localization: a comparison of patch descriptor performance

    , Pattern Recognition Letters, Vol: 66, Pages: 109-117, ISSN: 1872-7344

    Vision is one of the most important of the senses, and humans use it extensively during navigation. We evaluated different types of image and video frame descriptors that could be used to determine distinctive visual landmarks for localizing a person based on what is seen by a camera that they carry. To do this, we created a database containing over 3 km of video-sequences with ground-truth in the form of distance travelled along different corridors. Using this database, the accuracy of localization—both in terms of knowing which route a user is on—and in terms of position along a certain route, can be evaluated. For each type of descriptor, we also tested different techniques to encode visual structure and to search between journeys to estimate a user’s position. The techniques include single-frame descriptors, those using sequences of frames, and both color and achromatic descriptors. We found that single-frame indexing worked better within this particular dataset. This might be because the motion of the person holding the camera makes the video too dependent on individual steps and motions of one particular journey. Our results suggest that appearance-based information could be an additional source of navigational data indoors, augmenting that provided by, say, radio signal strength indicators (RSSIs). Such visual information could be collected by crowdsourcing low-resolution video feeds, allowing journeys made by different users to be associated with each other, and location to be inferred without requiring explicit mapping. This offers a complementary approach to methods based on simultaneous localization and mapping (SLAM) algorithms.

  • Journal article
    Zhang Z, Ferretti V, Guentan I, Moro A, Steinberg EA, Ye Z, Zecharia AY, Yu X, Vyssotski AL, Brickley SG, Yustos R, Pillidge ZE, Harding EC, Wisden W, Franks NPet al., 2015,

    Neuronal ensembles sufficient for recovery sleep and the sedative actions of alpha(2) adrenergic agonists

    , Nature Neuroscience, Vol: 18, Pages: 553-561, ISSN: 1546-1726
  • Conference paper
    Reichenbach T, Stefanovic A, Nin F, Hudspeth AJet al., 2015,

    Otoacoustic Emission Through Waves on Reissner's Membrane

    , 12th International Workshop on the Mechanics of Hearing, Publisher: AMER INST PHYSICS, ISSN: 0094-243X
  • Conference paper
    Reynolds S, Onativia J, Copeland CS, Schultz SR, Dragotti PLet al., 2015,

    Spike Detection Using FRI Methods and Protein Calcium Sensors: Performance Analysis and Comparisons

    , International Conference on Sampling Theory and Applications (SampTA), Publisher: IEEE, Pages: 533-537
  • Conference paper
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Associating Locations Between Indoor Journeys from Wearable Cameras

    , 13th European Conference on Computer Vision (ECCV), Publisher: SPRINGER-VERLAG BERLIN, Pages: 29-44, ISSN: 0302-9743
  • Conference paper
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Indoor Localisation with Regression Networks and Place Cell Models.

    , Publisher: BMVA Press, Pages: 147.1-147.1
  • Journal article
    Wisden W, Yu X, Zecharia A, Zhang Z, Yang Q, Yustos R, Jager P, Vyssotski AL, Maywood ES, Chesham JE, Ma Y, Brickley SG, Hastings MH, Franks NPet al., 2014,

    Circadian Factor BMAL1 in Histaminergic Neurons Regulates Sleep Architecture

    , Current Biology, Vol: 24, Pages: 1-7, ISSN: 1879-0445

    Circadian clocks allow anticipation of daily environmental changes [ 1 ]. The suprachiasmatic nucleus (SCN) houses the master clock, but clocks are also widely expressed elsewhere in the body [ 1 ]. Although some peripheral clocks have established roles [ 1 ], it is unclear what local brain clocks do [ 2, 3 ]. We tested the contribution of one putative local clock in mouse histaminergic neurons in the tuberomamillary nucleus to the regulation of the sleep-wake cycle. Histaminergic neurons are silent during sleep, and start firing after wake onset [ 4–6 ]; the released histamine, made by the enzyme histidine decarboxylase (HDC), enhances wakefulness [ 7–11 ]. We found that hdc gene expression varies with time of day. Selectively deleting the Bmal1 (also known as Arntl or Mop3 [ 12 ]) clock gene from histaminergic cells removes this variation, producing higher HDC expression and brain histamine levels during the day. The consequences include more fragmented sleep, prolonged wake at night, shallower sleep depth (lower nonrapid eye movement [NREM] δ power), increased NREM-to-REM transitions, hindered recovery sleep after sleep deprivation, and impaired memory. Removing BMAL1 from histaminergic neurons does not, however, affect circadian rhythms. We propose that for mammals with polyphasic/nonwake consolidating sleep, the local BMAL1-dependent clock directs appropriately timed declines and increases in histamine biosynthesis to produce an appropriate balance of wake and sleep within the overall daily cycle of rest and activity specified by the SCN.

  • Journal article
    Ruz ID, Schultz SR, 2014,

    Localising and classifying neurons from high density MEA recordings

    , JOURNAL OF NEUROSCIENCE METHODS, Vol: 233, Pages: 115-128, ISSN: 0165-0270
  • Journal article
    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.

  • Journal article
    Reichenbach T, Hudspeth AJ, 2014,

    The physics of hearing: fluid mechanics and the active process of the inner ear

    , REPORTS ON PROGRESS IN PHYSICS, Vol: 77, ISSN: 0034-4885
  • Conference paper
    Tchumatchenko T, Reichenbach T, 2014,

    A wave of cochlear bone deformation can underlie bone conduction and otoacoustic emissions

    , 12th International Workshop on the Mechanics of Hearing, Publisher: AIP Publishing LLC, ISSN: 0094-243X

    A sound signal is transmitted to the cochlea through vibration of the middle ear that induces a pressure difference across the cochlea’s elastic basilar membrane. In an alternative pathway for transmission, the basilar membrane can also be deflected by vibration of the cochlear bone, without participation of the middle ear. This second pathway, termed bone conduction, is increasingly used in commercial applications, namely in bone-conduction headphones that deliver sound through vibration of the skull. The mechanism of this transmission, however, remains unclear. Here, we study a cochlear model in which the cochlear bone is deformable. We show that deformation of the cochlear bone, such as resulting from bone stimulation, elicits a wave on the basilar membrane and can hence explain bone conduction. Interestingly, stimulation of the basilar membrane can in turn elicit a wave of deformation of the cochlear bone. We show that this has implications for the propagation of otoacoustic emissions: these can emerge from the cochlea through waves of bone deformation.

  • Journal article
    Luan S, Williams I, Constandinou TG, Nikolic Ket al., 2014,

    Neuromodulation: present and emerging methods

    , Frontiers of Neuroengineering, 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.

  • Journal article
    Williams I, Constandinou TG, 2014,

    Computationally Efficient Modelling of Proprioceptive Signals in the Upper Limb for Prostheses: a Simulation Study

    , Frontiers in Neuroscience, Vol: 8, Pages: 1-13

    Accurate models of proprioceptive neural patterns could one day play an important role in the creation of an intuitive proprioceptive neural prosthesis for amputees. This paper looks at combining efficient implementations of biomechanical and proprioceptor models in order to generate signals that mimic human muscular proprioceptive patterns for future experimental work in prosthesis feedback. A neuro-musculoskeletal model of the upper limb with 7 degrees of freedom and 17 muscles is presented and generates real time estimates of muscle spindle and Golgi Tendon Organ neural firing patterns. Unlike previous neuro-musculoskeletal models, muscle activation and excitation levels are unknowns in this application and an inverse dynamics tool (static optimisation) is integrated to estimate these variables. A proprioceptive prosthesis will need to be portable and this is incompatible with the computationally demanding nature of standard biomechanical and proprioceptor modelling. This paper uses and proposes a number of approximations and optimisations to make real time operation on portable hardware feasible. Finally technical obstacles to mimicking natural feedback for an intuitive proprioceptive prosthesis, as well as issues and limitations with existing models, are identified and discussed.

  • Journal article
    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, Pages: 1-15

    This work presents a new method that combines symbol dynamics methodologies with an Ngram algorithm for the detection and prediction of epileptic seizures. The presented approach specifically applies Ngram-based pattern recognition, after data pre-processing, with similarity metrics, including the Hamming distance and Needlman-Wunsch algorithm, for identifying unique patterns within epochs of time. Pattern counts within each epoch are used as measures to determine seizure detection and prediction markers. Using 623 hours of intracranial electrocorticogram recordings from 21 patients containing a total of 87 seizures, the sensitivity and false prediction/detection rates of this method are quantified. Results are quantified using individual seizures within each case for training of thresholds and prediction time windows. The statistical significance of the predictive power is further investigated. We show that the method presented herein, has significant predictive power in up to 100% of temporal lobe cases, with sensitivities of up to 70–100% and low false predictions (dependant on training procedure). The cases of highest false predictions are found in the frontal origin with 0.31–0.61 false predictions per hour and with significance in 18 out of 21 cases. On average, a prediction sensitivity of 93.81% and false prediction rate of approximately 0.06 false predictions per hour are achieved in the best case scenario. This compares to previous work utilising the same data set that has shown sensitivities of up to 40–50% for a false prediction rate of less than 0.15/hour.

  • Journal article
    Longden KD, Muzzu T, Cook DJ, Schultz SR, Krapp HGet al., 2014,

    Nutritional State Modulates the Neural Processing of Visual Motion

    , CURRENT BIOLOGY, Vol: 24, Pages: 890-895, ISSN: 0960-9822
  • Journal article
    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

    Electrical Neural Stimulation is the technique used to modulate neural activity by inducing an instantaneous charge imbalance. This is typically achieved by injecting a constant current and controlling the stimulation time. However, constant voltage stimulation is found to be more energy-efficient although it is challenging to control the amount of charge delivered. This paper presents a novel, fully-integrated circuit for facilitating charge-metering in constant voltage stimulation. It utilises two complementary stimulation paths. Each path includes a small capacitor, a comparator and a counter. They form a mixed-signal integrator that integrates the stimulation current onto the capacitor whilst monitoring its voltage against a threshold using the comparator. The pulses from the comparator are used to increment the counter and reset the capacitor. Therefore, by knowing the value of the capacitor, threshold voltage and output of the counter, the quantity of charge delivered can be calculated. The system has been fabricated in 0.18μm CMOS technology, occupying a total active area of 339μm×110μm. Experimental results were taken using: (1) a resistor-capacitor EEI model and (2) platinum electrodes with ringer solution. The viability of this method in recruiting action potentials has been demonstrated using a cuff electrode with Xenopus Sciatic nerve. For a 10nC target charge delivery, the results of (2) show a charge delivery error of 3.4% and a typical residual charge of 77.19pC without passive charge recycling. The total power consumption is 45μW. The performance is comparable with other publications. Therefore, the proposed stimulation method can be used as a new approach for neural stimulation.

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