Conference Paper

10-channel very low noise ENG amplifier system using CMOS technology

Department of Electronic and Electrical Engineering, University of Bath, Bath, England, United Kingdom
DOI: 10.1109/ISCAS.2005.1464696 Conference: Circuits and Systems, 2005. ISCAS 2005. IEEE International Symposium on
Source: IEEE Xplore

ABSTRACT In this paper the design, fabrication and testing of a 10-channel array of identical amplifiers suitable for velocity selective electroneurogram (ENG) recording is described. The overall gain per channel is 10,000 and the total input-referred rms noise in a bandwidth 1 Hz-5 kHz is 290 nV per channel. The active area is 12 mm2 and the power consumption is 24 mW from ±2.5 V power supplies.

  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper describes the design of an implantable system for velocity-selective electroneurogram (ENG) recording. The system, which relies on the availability of multielectrode nerve cuffs (MECs) consists of two CMOS ASICs. One ASIC called the electrode unit (EU) is a mixed analogue/digital signal acquisition system which is mounted directly on an MEC in order to optimize the interface between the two. It is linked to the other ASIC by means of a 5-core cable through which it receives power and commands in addition to transmitting data. The second ASIC, called the monitoring unit (MU) manages the interface between the EUs (each MU can control up to three EUs) and an RF transcutaneous link to the external signal processor. The ASICs are fabricated in 0.8μm CMOS technology. The EUs measure 3mm×4mm each and consume 105mW (35mW each), while the MU measures 1.5mm×2mm and consumes 4mW. The power consumption on the communication channels (including cable losses) between the MU and EUs is 129mW. A digital communication strategy between the two parts of the implanted system and the external controller is described.
    Analog Integrated Circuits and Signal Processing 01/2009; 58(2):91-104. DOI:10.1007/s10470-008-9233-2 · 0.40 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Sensory information coming from natural sensors and being propagated on afferent nerve fibers could be used as feedback for a more efficient closed-loop control of a functional electrical stimulation system. In order to extract and separate these signals according to their nerve fascicule origins, we propose a new architecture of a multipolar cuff electrode and an optimized integrated acquisition circuit. Concerning the electrode, we propose a specific configuration using a large number of poles in order to both reject parasitic signals, such as electromyogram and provide a maximum of recording channels in order to help the signal localization inside the nerve. Moreover, specific low-level analog signal processing was designed to extract the expected low-amplitude signal from its noisy environment. This signal processing is implemented in an ASIC that has to be implanted close to the electrode to achieve the best signal-to-noise ratio
    Neural Engineering, 2007. CNE '07. 3rd International IEEE/EMBS Conference on; 06/2007
  • [Show abstract] [Hide abstract]
    ABSTRACT: Parallel recording of micro-scale signals using an integrated system approach has become feasible with recent advances in technology. Practical applications include the recording of neural-signals in a brain-computer interface or in prosthetic implants. In an integrated circuit implementation the restriction in size and available power pose considerable challenges, especially in implanted devices. Furthermore, the provision of both high gain and excellent noise performance in the presence of input offset voltages are mandatory. The presented tutorial highlights design strategies for recording system optimization and compares the performance of actual system implementations with the best-case performance achievable in theory. Special consideration is given to the noise vs. power and offset-tolerance vs. noise trade-offs. An application dependent design strategy is proposed.
    Analog Integrated Circuits and Signal Processing 01/2009; 58(2):123-133. DOI:10.1007/s10470-008-9230-5 · 0.40 Impact Factor