Otoacoustic Emissions Simulated in the Time-Domain by a Hydroynamic Model of the Human Cochlea

Time-domain simulations of the response to click of a human ear show that, if the cochlear amplifier gain (CAG) is a smooth function of basilar-membrane (BM) position, the filtering performed by a middle ear with an irregular (non-smooth) transfer function suffices to produce irregular and long-lasting residual BM oscillations at selected frequencies. Feeding back to the middle ear through hydrodynamic coupling, these oscillations are detected as otoacoustic emissions (OAEs) in the ear canal. If, in addition, also the CAG profile is irregular, residual BM oscillations are even more irregular, often ensuing to self-sustaining oscillations at CAG irregularity loci. Correspondingly, transient evoked OAE spectra exhibit sharp peaks. If both the CAG and the middle-ear transfer function are smooth, residual BM oscillations are characterized by regular waveform, extinguish rapidly and do not generate appreciable emission. Simulating localized damage to the cochlear amplifier results in spontaneous emissions and stimulus-frequency OAEs, with typical modulation patterns, for inputs near hearing threshold

Efficient Universal Computing Architectures for Decoding Neural Activity

The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain– machine interfaces (BMIs). Such systems require input from tens to hundreds of brain-implanted recording electrodes in order to deliver robust and accurate performance; in serving that primary function they should also minimize power dissipation in order to avoid damaging neural tissue; and they should transmit data wirelessly in order to minimize the risk of infection associated with chronic, transcutaneous implants. Electronic architectures for brain– machine interfaces must therefore minimize size and power consumption, while maximizing the ability to compress data to be transmitted over limited-bandwidth wireless channels. Here we present a system of extremely low computational complexity, designed for real-time decoding of neural signals, and suited for highly scalable implantable systems. Our programmable architecture is an explicit implementation of a universal computing machine emulating the dynamics of a network of integrate-and-fire neurons; it requires no arithmetic operations except for counting, and decodes neural signals using only computationally inexpensive logic operations. The simplicity of this architecture does not compromise its ability to compress raw neural data by factors greater than . We describe a set of decoding algorithms based on this computational architecture, one designed to operate within an implanted system, minimizing its power consumption and data transmission bandwidth; and a complementary set of algorithms for learning, programming the decoder, and postprocessing the decoded output, designed to operate in an external, nonimplanted unit. The implementation of the implantable portion is estimated to require fewer than 5000 operations per second. A proof-of-concept, 32-channel field-programmable gate array (FPGA) implementation of this portion is consequently energy efficient. We validate the performance of our overall system by decoding electrophysiologic data from a behaving rodent.United States. National Institutes of Health (Grant NS056140

A battery-free tag for wireless monitoring of heart sounds

We have developed a wearable, battery-free tag that monitors heart sounds. The tag powers up by harvesting ambient RF energy, and contains a low-power integrated circuit, an antenna and up to four microphones. The chip, which consumes only 1.0 uW of power, generates digital events when the outputs of any of the microphones exceeds a programmable threshold voltage, combines such events together by using a programmable logic array, and transmits them to a base station by using backscatter modulation. The chip can also be programmed to trade-off microphone sensitivity for power consumption. In this paper, we demonstrate that the tag, when attached to the chest, can reliably measure heart rate at distances up to 7 m from an FCC-compliant RF power source. We also suggest how delays between signals measured by microphones at the wrist and neck can be used to provide information about relative blood-pressure variations

A Low-Power, Battery-Free Tag for Body Sensor Networks

A new tag for pervasive sensing applications consists of a custom integrated circuit, an antenna for radio frequency energy harvesting, and sensors for monitoring physiological parameters. This paper presents a wearable tag design that can monitor multiple signals. The tag generates an alarm when it suspects a patient emergency. To quickly cover a large portion of the population at risk, we kept the tag affordable (less than US$2 each when manufactured in volume), disposable, small, and easy to use. Such tags would be useful for hospitals, facilities for infants and the elderly, and ordinary homes to detect and alert caregivers to possible problems including SCA and SIDS

An Ultra-Low-Power Pulse Oximeter Implemented With an Energy-Efficient Transimpedance Amplifier

Pulse oximeters are ubiquitous in modern medicine to noninvasively measure the percentage of oxygenated hemoglobin in a patient's blood by comparing the transmission characteristics of red and infrared light-emitting diode light through the patient's finger with a photoreceptor. We present an analog single-chip pulse oximeter with 4.8-mW total power dissipation, which is an order of magnitude below our measurements on commercial implementations. The majority of this power reduction is due to the use of a novel logarithmic transimpedance amplifier with inherent contrast sensitivity, distributed amplification, unilateralization, and automatic loop gain control. The transimpedance amplifier, together with a photodiode current source, form a high-performance photoreceptor with characteristics similar to those found in nature, which allows LED power to be reduced. Therefore, our oximeter is well suited for portable medical applications, such as continuous home-care monitoring for elderly or chronic patients, emergency patient transport, remote soldier monitoring, and wireless medical sensing. Furthermore, our design obviates the need for an A-to-D and digital signal processor and leads to a small single-chip solution. We outline how extensions of our work could lead to submilliwatt oximeters

An Articulatory Speech-Prosthesis System

We investigate speech-coding strategies for brain-machine-interface (BMI) based speech prostheses. We present an articulatory speech-synthesis system using an experimental integrated-circuit vocal tract that models the human vocal tract. Our articulatory silicon vocal tract makes feasible the transmission of low bit-rate speech-coding parameters over a bandwidth-constrained body sensor network (BSN). To the best of our knowledge, this is the first articulatory speech-prosthesis system reported to date. We also present a speech-prosthesis simulator (SPS) as a means to generate realistic articulatory parameter sequences.National Institutes of Health (U.S.) (Grant NS056140

Biologically inspired silicon vocal tract

Electrical circuit models of biological systems provide an intuitive mechanism for engineers' understanding and are increasingly used to improve the performance of related technology. For example, visual processing performed by the retina can be modeled by a resistive network of interconnected photodetectors and analog processing elements. Complex bio-mechanical systems such as the heart, cochlea, and vocal tract can be modeled using electrical circuits by mapping pressure to voltage, volume velocity to current, and mechanical impedances to electrical impedances, and by representing valves with diodes. Silicon models of the retina1 have been used in machine vision systems and circuit models of the heart have been used to shed light on cardiac and circulatory malfunction in medicine. Silicon cochlea models have led to improved speech recognition in noise2 and low-power cochlear-implant processors for the deaf

A speech locked loop for cochlear implants and speech prostheses

We have previously described a feedback loop that combines an auditory processor with a low-power analog integrated-circuit vocal tract to create a speech-locked-loop. Here, we describe how the speech-locked loop can help improve speech recognition in noise by re-synthesizing clean speech from noisy speech. Therefore, it is potentially useful for improving speech recognition in noise, important in cochlear implant and other applications. We show that it can also produce good-quality speech with articulatory parameters, which are inherently low-dimensional and robust. Therefore, it is potentially also useful in brain-machine-based speech prostheses.National Institutes of Health (U.S.) (Grant number NS-056140)United States. Office of Naval Research (Grant number N00014-09-1-1015