- Browse by Author
Browsing by Author "Yoshida, Ken"
Now showing 1 - 10 of 31
Results Per Page
Sort Options
Item Accurate and representative decoding of the neural drive to muscles in humans with multi-channel intramuscular thin-film electrodes(Wiley, 2015-09-01) Muceli, Silvia; Poppendieck, Wigand; Negro, Francesco; Yoshida, Ken; Hoffmann, Klaus P.; Butler, Jane E.; Gandevia, Simon C.; Farina, Dario; Department of Biomedical Engineering, School of Engineering and TechnologyIntramuscular electrodes developed over the past 80 years can record the concurrent activity of only a few motor units active during a muscle contraction. We designed, produced and tested a novel multi-channel intramuscular wire electrode that allows in vivo concurrent recordings of a substantially greater number of motor units than with conventional methods. The electrode has been extensively tested in deep and superficial human muscles. The performed tests indicate the applicability of the proposed technology in a variety of conditions. The electrode represents an important novel technology that opens new avenues in the study of the neural control of muscles in humans. We describe the design, fabrication and testing of a novel multi-channel thin-film electrode for detection of the output of motoneurones in vivo and in humans, through muscle signals. The structure includes a linear array of 16 detection sites that can sample intramuscular electromyographic activity from the entire muscle cross-section. The structure was tested in two superficial muscles (the abductor digiti minimi (ADM) and the tibialis anterior (TA)) and a deep muscle (the genioglossus (GG)) during contractions at various forces. Moreover, surface electromyogram (EMG) signals were concurrently detected from the TA muscle with a grid of 64 electrodes. Surface and intramuscular signals were decomposed into the constituent motor unit (MU) action potential trains. With the intramuscular electrode, up to 31 MUs were identified from the ADM muscle during an isometric contraction at 15% of the maximal force (MVC) and 50 MUs were identified for a 30% MVC contraction of TA. The new electrode detects different sources from a surface EMG system, as only one MU spike train was found to be common in the decomposition of the intramuscular and surface signals acquired from the TA. The system also allowed access to the GG muscle, which cannot be analysed with surface EMG, with successful identification of MU activity. With respect to classic detection systems, the presented thin-film structure enables recording from large populations of active MUs of deep and superficial muscles and thus can provide a faithful representation of the neural drive sent to a muscle.Item Accurate simulation of cuff electrode stimulation predicting in-vivo strength-duration thresholds(Wiley, 2022) Lazorchak, Nathaniel; Horn, M. Ryne; Muzquiz, M. Ivette; Mintch, Landan M.; Yoshida, Ken; Biomedical Engineering, School of Engineering and TechnologyBackground: In-silico experiments used to optimize and inform how peripheral nerve based electrode designs perform hold the promise of greatly reducing the guesswork with new designs as well as the number of animals used to identify and prove promising designs. Given adequate realism, in-silico experiments offer the promise of identifying putative mechanisms that further inform exploration of novel stimulation and recording techniques and their interactions with bioelectric phenomena. However, despite using validated nerve fiber models, when applied to the more complex case of an implanted extracellular electrode, the in-silico experiments often do not compare quantitatively with the results of experiments conducted in in-vivo experiments. This suggests that the accuracy/realism of the environment and the lamination of the nerve bundle plays an important role in this discrepancy. This paper describes the sensitivity of in-silico models to the electrical parameter estimates and volume conductor type used. Methods: In-vivo work was performed on rat vagus nerves (N = 2) to characterize the strength-duration curve for various peaks identified in a compound nerve action potential (CAP) measured via a needle electrode. The vagus nerve has several distinct populations of nerve fiber calibers and types. Recruitment of a fiber caliber/type generates distinct peaks that can be identified, and whose conduction delay correlates to a conduction velocity. Peaks were identified by their recruitment thresholds and associated to their conduction velocities by the conduction delays of their peaks. An in-silico analog of the in-vivo experiment was constructed and experiments were run at the two extreme volume conductor cases: (1) The nerve in-saline, and (2) the nerve in-air. The specifically targeted electrical parameters were extraneural environment (in-air versus saline submersion), the resistivity (ρ) of the epineurium and perineurium, and the relative permittivity (εr ) of those same tissues. A time varying finite element method (FEM) model of the potential distribution vs time was quantified and projected onto a modified McIntyre, Richardson, and Grill (MRG), myelinated spinal nerve, active fiber model in NEURON to identify the threshold of activation as a function of stimulus pulse amplitude versus pulse width versus fiber diameter. The in-silico results were then compared to the in-vivo results. Results: The finite element method simulations spanned two macro environments: in-saline and in-air. For these environments, the resistivities for low and high frequencies as well as two different permittivity cases were used. Between these 8 cases unique cases it was found that the most accurate combination of those variables was the in-air environment for low-frequency resistivity (ρ0 ) and ex-vivo a measured permittivity (εr,measured ) from unpublished ex-vivo experiments in canine vagal nerve, achieving a high degree of convergence (r2 = 0.96). As the in-vivo work was conducted in in-air, the in-air boundary condition test case was convergent with the in-silico results. Conclusions: The results of this investigation suggest that increasing realism in simulations begets more accurate predictions. Of particular importance are (ρ) and extraneural environment, with reactive electrical parameters becoming important for input waveforms with energy in higher frequencies.Item Analysis of Heart Rate Variability During Focal Parasympathetic Drive of the Rat Baroreflex(2020-05) Bustamante, David J.; Schild, John; Yoshida, Ken; Salama, PaulAutonomic control of the heart results in variations in the intervals between heart beats, known as heart rate variability. One of the defining components of autonomic control is the baroreflex, a negative feedback controller that balances heart rate and blood pressure. The baroreflex is under constant command from the branches of the autonomic nervous system. To better understand how the autonomic nervous system commands the baroreflex, a baroreflex reflexogenic animal protocol was carried out. Heart rate variability analysis and baroreflex sensitivity were used to quantify the neural control of the heart. This thesis reconfirmed the existence of sexually dimorphic properties in the baroreflex through the use of heart rate variability analysis and baroreflex sensitivity. It was discovered that there are many caveats to utilizing heart rate variability analysis, which have to be addressed both in the experimental protocol and the signal processing technique. Furthermore, it was suggested that the slope method for quantifying baroreflex sensitivity also has many caveats, and that other baroreflex sensitivity methods are likely more optimal for quantifying sustained activation of the baroreflex. By utilizing various heart rate variability signal processing algorithms to assess autonomic tone in Sprague-Dawley rats during rest and sustained electrical activation of the baroreflex, the null hypothesis was rejected.Item Analysis of the Bioelectric Impedance of the Tissue-Electrode Interface Using a Novel Full-Spectrum Approach(2014-01-15) Sempsrott, David Robert; Yoshida, Ken; Salama, Paul; Berbari, Edward J.Non-invasive surface recording of bioelectric potentials continues to be an essential tool in a variety of research and medical diagnostic procedures. However, the integrity of these recordings, and hence the reliability of subsequent analysis, diagnosis, or recommendations based on the recordings, can be significantly compromised when various types of noise are allowed to penetrate the recording circuit and contaminate the signals. In particular, for bioelectric phenomena in which the amplitude of the biosignal is relatively low, such as muscle activity (typically on the order of millivolts) or neural traffic (microvolts), external noise may substantially contaminate or even completely overwhelm the signal. In such circumstances, the tissue-electrode interface is typically the primary point of signal contamination since its impedance is relatively high compared to the rest of the recording circuit. Therefore, in the recording of low-amplitude biological signals, it is of paramount importance to minimize the impedance of the tissue-electrode interface in order to consistently obtain low-noise recordings. The aims of the current work were (1) to complete the development of a set of tools for rapid, simple, and reliable full-spectrum characterization and analytical modeling of the complex impedance of the tissue-electrode interface, and (2) to characterize the interfacial impedance and signal-to-noise ratio (SNR) at the surface of the skin across a variety of preparation methods and determine a factor or set of factors that contribute most effectively to the reduction of tissue-electrode impedance and noise contamination during recording. Specifically, we desired to test an initial hypothesis that surface abrasion is the principal determining factor in skin preparation to achieve consistently low-impedance, low-noise recordings. During the course of this master’s study, (1) a system with portable, battery-powered hardware and robust acquisition/analysis software for broadband impedance characterization has been achieved, and (2) the effects of skin preparation methods on the impedance of the tissue-electrode interface and the SNR of surface electromyographic recordings have been systematically quantified and compared in human subjects. We found our hypothesis to be strongly supported by the results: the degree of surface abrasion was the only factor that could be correlated to significant differences in either the interfacial impedance or the SNR. Given these findings, we believe that abrasion holds the key to consistently obtaining a low-impedance contact interface and high-quality recordings and should thus be considered an essential component of proper skin preparation prior to attachment of electrodes for recording of small bioelectric surface potentials.Item Analysis of the efficacy of EPIONE therapies to treat phantom limb pain(2017-03) Comoglio, Caleb C.; Yoshida, KenThe primary objectives of this thesis are (1) to discuss the current understanding of phenomena associated with, proposed mechanisms of, and suggested treatments for amputation related pain, (2) to describe the software developed for analyzing results of a clinical study for the treatment of phantom limb pain (PLP), (3) to discuss the methods for a multi-center trial by the EPIONE consortium along with presenting preliminary results, and (4) to discuss the methods and results of a case study involving a new therapy modality for alleviating PLP. Each objective has been expanded into a chapter as described below. Chapter 1 serves as a literature review introducing the topic of amputation, associ- ated phenomena, and proposed mechanisms. The chapter also discusses the currently available treatments and the instruments used to measure PLP. Key topics include the definition of PLP, the prevalence of PLP, current treatment options for PLP, and experimental measurement of PLP. The final objective of this chapter is to introduce topics related to the investigation paradigm utilized for the studies following in Chapter 4 and Chapter 5. Therefore, a minor emphasis has been put on surface electrical stimulation (SES) and operant conditioning. As with any multi-center clinical study, coordination is key. Chapter 2 introduces the common clinical protocol (CCP) and methods of analysis for the clinical trials conducted by the EPIONE consortium. In order to analyze results in an automated fashion, a software tool was developed. This tool, the EPIONE Extraction Program (EEP) along with its extension the Group Analysis Module (GAM), is the focus of Chapter 3. A high-level overview of the requirements, process flow, and software testing are described. This chapter also discusses the methods of analysis for several self-report instruments used to determine effect size in Chapter 4 and Chapter 5. The outputs of the software tools make up the results presented and described in these chapters. In addition to the details included in Chapter 3, supplemental information is available in Appendix A and Appendix B, which are the detailed User Guides for the EEP and GAM. Chapter 4 reviews the pilot study data conducted by the EPIONE consortium. The primary and two secondary instruments used for analysis are discussed. This chapter provides a brief overview of results from the group. Each clinical site used slightly different variations of a common clinical protocol to better understand what effectively drives alleviation of PLP and to allow comparison of results. The work done at Indiana University - Purdue University Indianapolis (IUPUI) represents a small part of several other universities involved in the EPIONE consortium. Chapter 5 focuses on a case study at IUPUI with a more in-depth review of data collected throughout the study period. Using SES, we seek to reverse cortical reorganization by giving meaningful stimuli through existing circuitry. In this chapter the present work is discussed by introducing a case study in detail with an analysis of psychophysical data.Item Characterization of Biomimetic Spinal Cord Stimulations for Restoration of Sensory Feedback(2024-05) Zeiser, Sidnee L.; Yadav, Amol; Yoshida, Ken; Berbari, Edward; Sangha, Susan; Surowiec, RachelSensory feedback is a critical component for controlling neuroprosthetic devices and brain-machine interfaces (BMIs). A lack of sensory pathways can result in slow, coarse movements when using either of these technologies and, in addition, the user is unable to fully interact with the environment around them. Spinal cord stimulation (SCS) has shown potential for restoring these pathways, but traditional stimulation patterns with constant parameters fail to reproduce the complex neural firing necessary for conveying sensory information. Recent studies have proposed various biomimetic stimulation patterns as a more effective means of evoking naturalistic neural activity and, in turn, communicating meaningful sensory information to the brain. Unlike conventional patterns, biomimetic waveforms vary in frequency, amplitude, or pulse-width over the duration of the stimulation. To better understand the role of these parameters in sensory perception, this thesis worked to investigate the effects of SCS patterns utilizing stochastic frequency modulation, linear frequency modulation, and linear amplitude modulation. By calculating sensory detection thresholds and just-noticeable differences, the null hypothesis for stochastically-varied frequency and linear amplitude modulation techniques was rejected.Item Characterization of the Electrical Properties of Mammalian Peripheral Nerve Laminae(Wiley, 2023) Horn, M. Ryne; Vetter, Christian; Bashirullah, Rizwan; Carr, Mike; Yoshida, Ken; Biomedical Engineering, School of Engineering and TechnologyBackground and objective: The intrinsic electrical material properties of the laminar components of the mammalian peripheral nerve bundle are important parameters necessary for the accurate simulation of the electrical interaction between nerve fibers and neural interfaces. Improvements in the accuracy of these parameters improve the realism of the simulation and enables realistic screening of novel devices used for extracellular recording and stimulation of mammalian peripheral nerves. This work aims to characterize these properties for mammalian peripheral nerves to build upon the resistive parameter set established by Weerasuriya et al. in 1984 for amphibian somatic peripheral nerves (frog sciatic nerve) that is currently used ubiquitously in the in-silico peripheral nerve modeling community. Methods: A custom designed characterization chamber was implemented and used to measure the radial and longitudinal impedance between 10 mHz and 50 kHz of freshly excised canine vagus nerves using four-point impedance spectroscopy. The impedance spectra were parametrically fitted to an equivalent circuit model to decompose and estimate the components of the various laminae. Histological sections of the electrically characterized nerves were then made to quantify the geometry and laminae thicknesses of the perineurium and epineurium. These measured values were then used to calculate the estimated intrinsic electrical properties, resistivity and permittivity, from the decomposed resistances and reactances. Finally, the estimated intrinsic electrical properties were used in a finite element method (FEM) model of the nerve characterization setup to evaluate the realism of the model. Results: The geometric measurements were as follows: nerve bundle (1.6 ± 0.6 mm), major nerve fascicle diameter (1.3 ± 0.23 mm), and perineurium thickness (13.8 ± 2.1 μm). The longitudinal resistivity of the endoneurium was estimated to be 0.97 ± 0.05 Ωm. The relative permittivity and resistivity of the perineurium were estimated to be 2018 ± 391 and 3.75 kΩm ± 981 Ωm, respectively. The relative permittivity and resistivity of the epineurium were found to be 9.4 × 106 ± 8.2 × 106 and 55.0 ± 24.4 Ωm, respectively. The root mean squared (RMS) error of the experimentally obtained values when used in the equivalent circuit model to determine goodness of fit against the measured impedance spectra was found to be 13.0 ± 10.7 Ω, 2.4° ± 1.3°. The corner frequency of the perineurium and epineurium were found to be 2.6 ± 1.0 kHz and 368.5 ± 761.9 Hz, respectively. A comparison between the FEM model in-silico impedance experiment against the ex-vivo methods had a RMS error of 159.0 ± 95.4 Ω, 20.7° ± 9.8°. Conclusion: Although the resistive values measured in the mammalian nerve are similar to those of the amphibian model, the relative permittivity of the laminae bring new information about the reactance and the corner frequency (frequency at peak reactance) of the peripheral nerve. The measured and estimated corner frequency are well within the range of most bioelectric signals, and are important to take into account when modeling the nerve and neural interfaces.Item A Comparative Analysis of Local and Global Peripheral Nerve Mechanical Properties During Cyclical Tensile Testing(2022-05) Doering, Onna Marie; Yoshida, Ken; Wallace, Joseph; Goodwill, AdamUnderstanding the mechanical properties of peripheral nerves is essential for chronically implanted device design. The work in this thesis aimed to understand the relationship between local deformation responses to global strain changes in peripheral nerves. A custom-built mechanical testing rig and sample holder enabled an improved cyclical uniaxial tensile testing environment on rabbit sciatic nerves (N=5). A speckle was placed on the surface of the nerve and recorded with a microscope camera to track local deformations. The development of a semi-automated digital image processing algorithm systematically measured local speckle dimension and nerve diameter changes. Combined with the measured force response, local and global strain values constructed a stress-strain relationship and corresponding elastic modulus. Preliminary exploration of models such as Fung and 2-Term Mooney-Rivlin confirmed the hyperelastic nature of the nerve. The results of strain analysis show that, on average, local strain levels were approximately five times smaller than globally measured strains; however, the relationship was dependent on global strain magnitude. Elastic modulus values corresponding to ~9% global strains were 2.070 ± 1.020 MPa globally and 10.15 ± 4 MPa locally. Elastic modulus values corresponding to ~6% global strains were 0.173 ± 0.091 MPa globally and 1.030 ± 0.532 MPa locally.Item Continuous characterization of universal invertible amplifier using source noise(2017-12) Ahmed, Chandrama; Yoshida, Ken; Berbari, Edward; Salama, PaulWith passage of time and repeated usage of a system, component values that make up the system parameters change, causing errors in its functional output. In order to ensure the fidelity of the results derived from these systems it is thus very important to keep track of the system parameters while being used. This thesis introduces a method for tracking the existing system parameters while the system was being used using the inherent noise of its signal source. Kalman filter algorithm is used to track the inherent noise response to the system and use that response to estimate the system parameters. In this thesis this continuous characterization scheme has been used on a Universal Invertible Amplifier (UIA). Current biomedical research as well as diagnostic medicine depend a lot on shape profile of bio-electric signals of different sources, for example heart, muscle, nerve, brain etc. making it very important to capture the different event of these signals without the distortion usually introduced by the filtering of the amplifier system. The Universal Invertible Amplifier extracts the original signal in electrodes by inverting the filtered and compressed signal while its gain bandwidth profile allows it to capture from the entire bandwidth of bioelectric signals. For this inversion to be successful the captured compressed and filtered signals needs to be inverted with the actual system parameters that the system had during capturing the signals, not its original parameters. The continuous characterization scheme introduced in this thesis is aimed at knowing the system parameters of the UIA by tracking the response of its source noise and estimating its transfer function from that. Two types of source noises have been tried out in this method, an externally added noise that was digitally generated and a noise that inherently contaminates the signals the system is trying to capture. In our cases, the UIA was used to capture nerve activity from vagus nerve where the signal was contaminated with electrocardiogram signals providing us with a well-defined inherent noise whose response could be tracked with Kalman Filter and used to estimate the transfer function of UIA. The transfer function estimation using the externally added noise did not produce good results but could be improved by means that can be explored as future direction of this project. However continuous characterization using the inherent noise, a bioelectric signal, was successful producing transfer function estimates with minimal error. Thus this thesis was successful to introduce a novel approach for system characterization using bio-signal contamination.Item Developing a Neural Signal Processor Using the Extended Analog Computer(2013-08-21) Soliman, Muller Mark; Yoshida, Ken; Eberhart, Russell C.; Mills, Jonathan W. (Jonathan Wayne); Berbari, Edward J.Neural signal processing to decode neural activity has been an active research area in the last few decades. The next generation of advanced multi-electrode neuroprosthetic devices aim to detect a multiplicity of channels from multiple electrodes, making the relatively time-critical processing problem massively parallel and pushing the computational demands beyond the limits of current embedded digital signal processing (DSP) techniques. To overcome these limitations, a new hybrid computational technique was explored, the Extended Analog Computer (EAC). The EAC is a digitally confgurable analog computer that takes advantage of the intrinsic ability of manifolds to solve partial diferential equations (PDEs). They are extremely fast, require little power, and have great potential for mobile computing applications. In this thesis, the EAC architecture and the mechanism of the formation of potential/current manifolds was derived and analyzed to capture its theoretical mode of operation. A new mode of operation, resistance mode, was developed and a method was devised to sample temporal data and allow their use on the EAC. The method was validated by demonstration of the device solving linear diferential equations and linear functions, and implementing arbitrary finite impulse response (FIR) and infinite impulse response (IIR) linear flters. These results were compared to conventional DSP results. A practical application to the neural computing task was further demonstrated by implementing a matched filter with the EAC simulator and the physical prototype to detect single fiber action potential from multiunit data streams derived from recorded raw electroneurograms. Exclusion error (type 1 error) and inclusion error (type 2 error) were calculated to evaluate the detection rate of the matched filter implemented on the EAC. The detection rates were found to be statistically equivalent to that from DSP simulations with exclusion and inclusion errors at 0% and 1%, respectively.