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      An Adaptive Averaging Low Noise Front-End for Central and Peripheral Nerve Recording

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          Abstract

          <p class="first" id="P1">An adaptive averaging low noise analog front-end (AFE) is presented for central and peripheral nerve recording applications. The proposed topology allows users to trade off, on the fly, between input referred noise and the number of channels via averaging. The new low noise amplifier (LNA) utilizes a complementary doubled input transconductance ( <i>g</i> <sub> <i>m</i> </sub>) topology to effectively increase the noise efficiency factor (NEF) without chopping or use of a costly BiCMOS process. It addresses a disadvantage of the doubled- <i>g</i> <sub> <i>m</i> </sub> technique by a high input impedance DC-coupled LNA and saves on-chip space for higher density by eliminating AC-coupling capacitors. The proposed technique is particularly suitable for ultra-low noise multichannel recording from the peripheral nervous system (PNS) with channel selection analog multiplexer, where input signal is in tens of μV. A 32-ch proof-of-concept-prototype AFE was fabricated in a 5M2P 130-nm standard CMOS process, occupying 2.4 × 2.5 mm <sup>2</sup> together with its control block. The prototype LNA consumes 11 μW from a 1 V supply, providing 3.0 μVrms input referred noise with 61 ΜΩ input impedance, which are desirable for high SNR, to be further improved by the adaptive averaging technique. </p>

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          Most cited references14

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          A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems.

          Considerable scientific and technological efforts have been devoted to develop neuroprostheses and hybrid bionic systems that link the human nervous system with electronic or robotic prostheses, with the main aim of restoring motor and sensory functions in disabled patients. A number of neuroprostheses use interfaces with peripheral nerves or muscles for neuromuscular stimulation and signal recording. Herein, we provide a critical overview of the peripheral interfaces available and trace their use from research to clinical application in controlling artificial and robotic prostheses. The first section reviews the different types of non-invasive and invasive electrodes, which include surface and muscular electrodes that can record EMG signals from and stimulate the underlying or implanted muscles. Extraneural electrodes, such as cuff and epineurial electrodes, provide simultaneous interface with many axons in the nerve, whereas intrafascicular, penetrating, and regenerative electrodes may contact small groups of axons within a nerve fascicle. Biological, technological, and material science issues are also reviewed relative to the problems of electrode design and tissue injury. The last section reviews different strategies for the use of information recorded from peripheral interfaces and the current state of control neuroprostheses and hybrid bionic systems.
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            A low-power low-noise cmos for amplifier neural recording applications

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              Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array.

              The ongoing pilot clinical trial of the BrainGate neural interface system aims in part to assess the feasibility of using neural activity obtained from a small-scale, chronically implanted, intracortical microelectrode array to provide control signals for a neural prosthesis system. Critical questions include how long implanted microelectrodes will record useful neural signals, how reliably those signals can be acquired and decoded, and how effectively they can be used to control various assistive technologies such as computers and robotic assistive devices, or to enable functional electrical stimulation of paralyzed muscles. Here we examined these questions by assessing neural cursor control and BrainGate system characteristics on five consecutive days 1000 days after implant of a 4 × 4 mm array of 100 microelectrodes in the motor cortex of a human with longstanding tetraplegia subsequent to a brainstem stroke. On each of five prospectively-selected days we performed time-amplitude sorting of neuronal spiking activity, trained a population-based Kalman velocity decoding filter combined with a linear discriminant click state classifier, and then assessed closed-loop point-and-click cursor control. The participant performed both an eight-target center-out task and a random target Fitts metric task which was adapted from a human-computer interaction ISO standard used to quantify performance of computer input devices. The neural interface system was further characterized by daily measurement of electrode impedances, unit waveforms and local field potentials. Across the five days, spiking signals were obtained from 41 of 96 electrodes and were successfully decoded to provide neural cursor point-and-click control with a mean task performance of 91.3% ± 0.1% (mean ± s.d.) correct target acquisition. Results across five consecutive days demonstrate that a neural interface system based on an intracortical microelectrode array can provide repeatable, accurate point-and-click control of a computer interface to an individual with tetraplegia 1000 days after implantation of this sensor.
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                Author and article information

                Journal
                IEEE Transactions on Circuits and Systems II: Express Briefs
                IEEE Trans. Circuits Syst. II
                Institute of Electrical and Electronics Engineers (IEEE)
                1549-7747
                1558-3791
                July 2018
                July 2018
                : 65
                : 7
                : 839-843
                Article
                10.1109/TCSII.2017.2725988
                6338471
                30666177
                6911b45f-4b0a-44cc-a5fc-05a348320d0a
                © 2018

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