293 research outputs found

    A High TCMRR, Inherently Charge Balanced Bidirectional Front-End for Multichannel Closed-Loop Neuromodulation

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    This paper describes a multichannel bidirectional front-end for implantable closed-loop neuromodulation. Stimulation artefacts are reduced by way of a 4-channel H-bridge current source sharing stimulator front-end that minimizes residual charge drops in the electrodes via topology-inherent charge balancing. A 4-channel chopper front-end is capable of multichannel recording in the presence of artefacts as a result of its high total common-mode rejection ratio (TCMRR) that accounts for CMRR degradation due to electrode mismatch. Experimental verification of a prototype fabricated in a standard 180 nm process shows a stimulator front-end with 0.059% charge balance and 0.275 nA DC current error. The recording front-end consumes 3.24 µW, tolerates common-mode interference up to 1 Vpp and shows a TCMRR > 66 dB for 500 mVpp inputs.Ministerio de Economía y Competitividad TEC2016-80923-POffice of Naval Research (USA) N00014111031

    Investigation of the Hammerstein hypothesis in the modeling of electrically stimulated muscle

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    To restore functional use of paralyzed muscles by automatically controlled stimulation, an accurate quantitative model of the stimulated muscles is desirable. The most commonly used model for isometric muscle has had a Hammerstein structure, in which a linear dynamic block is preceded by a static nonlinear function, To investigate the accuracy of the Hammerstein model, the responses to a pseudo-random binary sequence (PRBS) excitation of normal human plantarflexors, stimulated with surface electrodes, were used to identify a Hammerstein model but also four local models which describe the responses to small signals at different mean levels of activation. Comparison of the local models with the Linearized Hammerstein model showed that the Hammerstein model concealed a fivefold variation in the speed of response. Also, the small-signal gain of the Hammerstein model was in error by factors up to three. We conclude that, despite the past widespread use of the Hammerstein model, it is not an accurate representation of isometric muscle. On the other hand, local models, which are more accurate predictors, can be identified from the responses to short PRBS sequences. The utility of local models for controller design is discussed

    A Bidirectional ASIC for Active Microchannel Neural Interfaces

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    Closed-loop neural prostheses have been widely used as a therapeutic strategy for a range of neurological, inflammatory, and cardiac disorders. Vagus nerve stimulation has shown promising results for the monitoring and treatment of post-operation symptoms of heart transplant recipients. A prime candidate for selective control of vagal fibres is the microchannel neural interface (MNI), which provides a suitable environment for neural growth and enables effective control of the neural activity in a bidirectional system. This paper presents the design and simulation of an ASIC in 180-nm high-voltage CMOS technology, capable of concurrent stimulation and neural recording with artifact reduction in a seven-channel MNI. The analog front-end amplifies action potentials with a gain of 40 dB, presenting a common-mode rejection ratio of 81 dB at 1 kHz and a noise efficiency factor of 5.13 over the 300 Hz to 5 kHz recording bandwidth. A 42-V-compliant stimulation module operates concurrently and independently across the seven channels

    Design of Integrated Neural/Modular Stimulators

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    Ph.DDOCTOR OF PHILOSOPH

    Wired, wireless and wearable bioinstrumentation for high-precision recording of bioelectrical signals in bidirectional neural interfaces

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    It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. However, the design and realisation of an instrument that can precisely record weak bioelectrical signals in the presence of strong interference stemming from a noisy clinical environment is one of the most difficult challenges associated with the strategy of monitoring bioelectrical signals for diagnostic purposes. Moreover, since patients often have to cope with the problem of limited mobility being connected to bulky and mains-powered instruments, there is a growing demand for small-sized, high-performance and ambulatory biopotential acquisition systems in the Intensive Care Unit (ICU) and in High-dependency wards. Furthermore, electrical stimulation of specific target brain regions has been shown to alleviate symptoms of neurological disorders, such as Parkinson’s disease, essential tremor, dystonia, epilepsy etc. In recent years, the traditional practice of continuously stimulating the brain using static stimulation parameters has shifted to the use of disease biomarkers to determine the intensity and timing of stimulation. The main motivation behind closed-loop stimulation is minimization of treatment side effects by providing only the necessary stimulation required within a certain period of time, as determined from a guiding biomarker. Hence, it is clear that high-quality recording of local field potentials (LFPs) or electrocorticographic (ECoG) signals during deep brain stimulation (DBS) is necessary to investigate the instantaneous brain response to stimulation, minimize time delays for closed-loop neurostimulation and maximise the available neural data. To our knowledge, there are no commercial, small, battery-powered, wearable and wireless recording-only instruments that claim the capability of recording ECoG signals, which are of particular importance in closed-loop DBS and epilepsy DBS. In addition, existing recording systems lack the ability to provide artefact-free high-frequency (> 100 Hz) LFP recordings during DBS in real time primarily because of the contamination of the neural signals of interest by the stimulation artefacts. To address the problem of limited mobility often encountered by patients in the clinic and to provide a wide variety of high-precision sensor data to a closed-loop neurostimulation platform, a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless multi-instrument (55 × 80 mm2) was designed and developed. The performance of the realised instrument was assessed by conducting both ex vivo and in vivo experiments. The combination of desirable features and capabilities of this instrument, namely its small size (~one business card), its enhanced recording capabilities, its increased processing capabilities, its manufacturability (since it was designed using discrete off-the-shelf components), the wide bandwidth it offers (0.5 – 500 Hz) and the plurality of bioelectrical signals it can precisely record, render it a versatile tool to be utilized in a wide range of applications and environments. Moreover, in order to offer the capability of sensing and stimulating via the same electrode, novel real-time artefact suppression methods that could be used in bidirectional (recording and stimulation) system architectures are proposed and validated. More specifically, a novel, low-noise and versatile analog front-end (AFE), which uses a high-order (8th) analog Chebyshev notch filter to suppress the artefacts originating from the stimulation frequency, is presented. After defining the system requirements for concurrent LFP recording and DBS artefact suppression, the performance of the realised AFE is assessed by conducting both in vitro and in vivo experiments using unipolar and bipolar DBS (monophasic pulses, amplitude ranging from 3 to 6 V peak-to-peak, frequency 140 Hz and pulse width 100 µs). Under both in vitro and in vivo experimental conditions, the proposed AFE provided real-time, low-noise and artefact-free LFP recordings (in the frequency range 0.5 – 250 Hz) during stimulation. Finally, a family of tunable hardware filter designs and a novel method for real-time artefact suppression that enables wide-bandwidth biosignal recordings during stimulation are also presented. This work paves the way for the development of miniaturized research tools for closed-loop neuromodulation that use a wide variety of bioelectrical signals as control signals.Open Acces

    High Efficiency Power Management Unit for Implantable Optical-Electrical Stimulators

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    Battery-less active implantable devices are of interest because they offer longer life span and eliminate costly battery replacement surgical interventions. This is possible as a result of advances in inductive power transfer and development of power management circuits to maximize the overall power transfer and provide various voltage levels for multi-functional implantable devices. Rehabilitation therapy using optical stimulation of genetically modified peripheral neurons requires high current loads. Standard rectification topologies are inefficient and have associated voltage drops unsuited for miniaturized implants. This paper presents an integrated power management unit (PMU) for an optical-electrical stimulator to be used in the treatment of motor neurone disease. It includes a power-efficient regulating rectifier with a novel body biased high-speed comparator providing 3.3 V for the operation of the stimulator, a 3-stage latch-up charge pump with 12 V output for the input stage of the optical-electrical stimulator, and 1.8 V for digital control logic. The chip was fabricated in a 0.18 μm CMOS process. Measured results show that for a regulated output of 3.3 V delivering 30.3 mW power, the peak power conversion efficiency is 84.2% at 6.78 MHz inductive link tunable frequency reducing to 70.3% at 13.56 MHz. The charge pump with on chip capacitors has 90.9% measured voltage conversion efficiency

    A Multi-Channel Stimulator With High-Resolution Time-to-Current Conversion for Vagal-Cardiac Neuromodulation

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    This paper presents an integrated stimulator for a cardiac neuroprosthesis aiming to restore the parasympathetic control after heart transplantation. The stimulator is based on time-to-current conversion. Instead of the conventional current mode digital-to-analog converter (DAC) that uses ten of microamp for biasing, the proposed design uses a novel capacitor time-based DAC offering close to 10 bit of current amplitude resolution while using only a bias current 250 nA. The stimulator chip was design in a 0.18 m CMOS high-voltage (HV) technology. It consists of 16 independent channels, each capable of delivering 550 A stimulus current under a HV output stage that can be operated up to 30 V. Featuring both power efficiency and high-resolution current amplitude stimulation, the design is suitable for multi-channel neural simulation applications

    Advances in Microelectronics for Implantable Medical Devices

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    Implantable medical devices provide therapy to treat numerous health conditions as well as monitoring and diagnosis. Over the years, the development of these devices has seen remarkable progress thanks to tremendous advances in microelectronics, electrode technology, packaging and signal processing techniques. Many of today’s implantable devices use wireless technology to supply power and provide communication. There are many challenges when creating an implantable device. Issues such as reliable and fast bidirectional data communication, efficient power delivery to the implantable circuits, low noise and low power for the recording part of the system, and delivery of safe stimulation to avoid tissue and electrode damage are some of the challenges faced by the microelectronics circuit designer. This paper provides a review of advances in microelectronics over the last decade or so for implantable medical devices and systems. The focus is on neural recording and stimulation circuits suitable for fabrication in modern silicon process technologies and biotelemetry methods for power and data transfer, with particular emphasis on methods employing radio frequency inductive coupling. The paper concludes by highlighting some of the issues that will drive future research in the field
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