24 research outputs found

    A neural probe with up to 966 electrodes and up to 384 configurable channels in 0.13 μm SOI CMOS

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    In vivo recording of neural action-potential and local-field-potential signals requires the use of high-resolution penetrating probes. Several international initiatives to better understand the brain are driving technology efforts towards maximizing the number of recording sites while minimizing the neural probe dimensions. We designed and fabricated (0.13-μm SOI Al CMOS) a 384-channel configurable neural probe for large-scale in vivo recording of neural signals. Up to 966 selectable active electrodes were integrated along an implantable shank (70 μm wide, 10 mm long, 20 μm thick), achieving a crosstalk of −64.4 dB. The probe base (5 × 9 mm2) implements dual-band recording and a 1

    Quantification of clinically applicable stimulation parameters for precision near-organ neuromodulation of human splenic nerves

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    Abstract: Neuromodulation is a new therapeutic pathway to treat inflammatory conditions by modulating the electrical signalling pattern of the autonomic connections to the spleen. However, targeting this sub-division of the nervous system presents specific challenges in translating nerve stimulation parameters. Firstly, autonomic nerves are typically embedded non-uniformly among visceral and connective tissues with complex interfacing requirements. Secondly, these nerves contain axons with populations of varying phenotypes leading to complexities for axon engagement and activation. Thirdly, clinical translational of methodologies attained using preclinical animal models are limited due to heterogeneity of the intra- and inter-species comparative anatomy and physiology. Here we demonstrate how this can be accomplished by the use of in silico modelling of target anatomy, and validation of these estimations through ex vivo human tissue electrophysiology studies. Neuroelectrical models are developed to address the challenges in translation of parameters, which provides strong input criteria for device design and dose selection prior to a first-in-human trial

    Error correction algorithm for high accuracy bio-impedance measurement in wearable healthcare applications

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    Implantable and ambulatory measurement of physiological signals such as Bio-impedance using miniature biomedical devices needs careful tradeoff between limited power budget, measurement accuracy and complexity of implementation. This paper addresses this tradeoff through an extensive analysis of different stimulation and demodulation techniques for accurate Bio-impedance measurement. Three cases are considered for rigorous analysis of a generic impedance model, with multiple poles, which is stimulated using a square/sinusoidal current and demodulated using square/sinusoidal clock. For each case, the error in determining pole parameters (resistance and capacitance) is derived and compared. An error correction algorithm is proposed for square wave demodulation which reduces the peak estimation error from 9.3% to 1.3% for a simple tissue model. Simulation results in Matlab using ideal RC values show an average accuracy of for single pole and for two pole RC networks. Measurements using ideal components for a single pole model gives an overall and readings from saline phantom solution (primarily resistive) gives an . A Figure of Merit is derived based on ability to accurately resolve multiple poles in unknown impedance with minimal measurement points per decade, for given frequency range and supply current budget. This analysis is used to arrive at an optimal tradeoff between accuracy and power. Results indicate that the algorithm is generic and can be used for any application that involves resolving poles of an unknown impedance. It can be implemented as a post-processing technique for error correction or even incorporated into wearable signal monitoring ICs

    A 60 mu W 60 nV/root Hz readout front-end for portable biopotential acquisition systems

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    There is a growing demand for low-power, small-size and ambulatory biopotential acquisition systems. A crucial and important block of this acquisition system is the analog readout front-end. We have implemented a low-power and low-noise readout front-end with configurable characteristics for Electroencephalogram (EEG), Electrocardiogram (ECG), and Electromyogram (EMG) signals. Key to its performance is the new AC-coupled chopped instrumentation amplifier (ACCIA), which uses a low power current feedback instrumentation amplifier (U). Thus, while chopping filters the 1/f noise of CMOS transistors and increases the CMRR, AC coupling is capable of rejecting differential electrode offset (DEO) up to +/- 50 mV from conventional Ag/AgCl electrodes. The ACCIA achieves 120 dB CMRR and 57 nV/root Hz input-referred voltage noise density, while consuming 11.1 mu A from a 3 V supply. The chopping spike filter (CSF) stage filters the chopping spikes generated by the input chopper of ACCIA and the digitally controllable variable gain stage is used to set the gain and the bandwidth of the front-end. The front-end is implemented in a 0.5 mu m CMOS process. Total current consumption is 20 mu A from 3V.status: publishe

    A 0.6-V, 0.015-mm2, time-based ECG readout for ambulatory applications in 40-nm CMOS

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    © 2016 IEEE. A scalable time-based analog front end in 40-nm CMOS is presented for ECG readout for ambulatory applications. The main challenge addressed is achieving a large dynamic range readout (necessary to handle large signals during motion) in a power and area-efficient manner at low voltage supplies while also tackling the challenges of increase in flicker noise and gate-leakage current. Demonstrated results show a significant improvement in ac-dynamic range without compromising on area (0.015 mm2) and power consumption (3.3∼ μW). This paper will be relevant toward developing low-cost, low-power sensor system-on-chips required for wearable biomedical applications.status: publishe

    A 15-Channel digital active electrode system for multi-parameter biopotential measurement

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    This paper presents a digital active electrode (DAE) system for multi-parameter biopotential signal acquisition in portable and wearable devices. It is built around an IC that performs analog signal processing and digitization with the helpof on-chip instrumentation amplifiers, a 12 bit ADC and a digital interface. Via a standard bus, up to 16 digital active electrodes (15-channels) can be connected to a commercially available microcontroller, thus significantly reducing systemcomplexity and cost. In addition, the DAE utilizes an innovative functionally DC-coupled amplifier to preserve input DC signal, while still achieving state-of-the-art performance: 60 nV/sqrt(Hz) input-referred noise and 350 mV electrode-offset tolerance. A common-mode feedforward scheme improves the CMRR of anAE pair from 40 dB to maximum 102 dB.Electronic Instrumentatio
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