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Description
The use of electromyography (EMG) signals to characterize muscle fatigue has been widely accepted. Initial work on characterizing muscle fatigue during isometric contractions demonstrated that its frequency decreases while its amplitude increases with the onset of fatigue. More recent work concentrated on developing techniques to characterize dynamic contractions for use

The use of electromyography (EMG) signals to characterize muscle fatigue has been widely accepted. Initial work on characterizing muscle fatigue during isometric contractions demonstrated that its frequency decreases while its amplitude increases with the onset of fatigue. More recent work concentrated on developing techniques to characterize dynamic contractions for use in clinical and training applications. Studies demonstrated that as fatigue progresses, the EMG signal undergoes a shift in frequency, and different physiological mechanisms on the possible cause of the shift were considered. Time-frequency processing, using the Wigner distribution or spectrogram, is one of the techniques used to estimate the instantaneous mean frequency and instantaneous median frequency of the EMG signal using a variety of techniques. However, these time-frequency methods suffer either from cross-term interference when processing signals with multiple components or time-frequency resolution due to the use of windowing. This study proposes the use of the matching pursuit decomposition (MPD) with a Gaussian dictionary to process EMG signals produced during both isometric and dynamic contractions. In particular, the MPD obtains unique time-frequency features that represent the EMG signal time-frequency dependence without suffering from cross-terms or loss in time-frequency resolution. As the MPD does not depend on an analysis window like the spectrogram, it is more robust in applying the timefrequency features to identify the spectral time-variation of the EGM signal.
ContributorsAustin, Hiroko (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Kovvali, Narayan (Committee member) / Muthuswamy, Jitendran (Committee member) / Arizona State University (Publisher)
Created2012
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Description
Advances in implantable MEMS technology has made possible adaptive micro-robotic implants that can track and record from single neurons in the brain. Development of autonomous neural interfaces opens up exciting possibilities of micro-robots performing standard electrophysiological techniques that would previously take researchers several hundred hours to train and achieve the

Advances in implantable MEMS technology has made possible adaptive micro-robotic implants that can track and record from single neurons in the brain. Development of autonomous neural interfaces opens up exciting possibilities of micro-robots performing standard electrophysiological techniques that would previously take researchers several hundred hours to train and achieve the desired skill level. It would result in more reliable and adaptive neural interfaces that could record optimal neural activity 24/7 with high fidelity signals, high yield and increased throughput. The main contribution here is validating adaptive strategies to overcome challenges in autonomous navigation of microelectrodes inside the brain. The following issues pose significant challenges as brain tissue is both functionally and structurally dynamic: a) time varying mechanical properties of the brain tissue-microelectrode interface due to the hyperelastic, viscoelastic nature of brain tissue b) non-stationarities in the neural signal caused by mechanical and physiological events in the interface and c) the lack of visual feedback of microelectrode position in brain tissue. A closed loop control algorithm is proposed here for autonomous navigation of microelectrodes in brain tissue while optimizing the signal-to-noise ratio of multi-unit neural recordings. The algorithm incorporates a quantitative understanding of constitutive mechanical properties of soft viscoelastic tissue like the brain and is guided by models that predict stresses developed in brain tissue during movement of the microelectrode. An optimal movement strategy is developed that achieves precise positioning of microelectrodes in the brain by minimizing the stresses developed in the surrounding tissue during navigation and maximizing the speed of movement. Results of testing the closed-loop control paradigm in short-term rodent experiments validated that it was possible to achieve a consistently high quality SNR throughout the duration of the experiment. At the systems level, new generation of MEMS actuators for movable microelectrode array are characterized and the MEMS device operation parameters are optimized for improved performance and reliability. Further, recommendations for packaging to minimize the form factor of the implant; design of device mounting and implantation techniques of MEMS microelectrode array to enhance the longevity of the implant are also included in a top-down approach to achieve a reliable brain interface.
ContributorsAnand, Sindhu (Author) / Muthuswamy, Jitendran (Thesis advisor) / Tillery, Stephen H (Committee member) / Buneo, Christopher (Committee member) / Abbas, James (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Microelectrodes have been used as the neural interface to record brain's neural activities. Most of these electrodes are fixed positioned. Neural signal normally degrades over time due to the body immune response and brain micromotion that move the neurons away from the microelectrode. MEMS technology under SUMMiT VTM processes has

Microelectrodes have been used as the neural interface to record brain's neural activities. Most of these electrodes are fixed positioned. Neural signal normally degrades over time due to the body immune response and brain micromotion that move the neurons away from the microelectrode. MEMS technology under SUMMiT VTM processes has developed miniaturized version of moveable microelectrodes that have the ability to recover the neural signal degradation by searching new cluster of neurons. To move the MEMS microelectrode a combination of four voltage waveforms must be applied to four thermally actuated microactuators. Previous design has used OmneticTM interconnect to transfer the waveforms from the external signal generators to the MEMS device. Unfortunately, the mechanism to attach and detach the OmneticTM interconnect introduce mechanical stress into the brain tissue that often caused raptures in the blood vessel. The goal of this project is to create an integrated System-On-Package Signal Generator that can be implanted on the brain of a rodent. A wireless system and a microcontroller are integrated together with the signal generators. The integrated system can be used to generate a series of voltage waveforms that can be customized to drive an array of MEMS movable microelectrodes when a triggered signal is received wirelessly. 3D stacking technique has been used to develop this Integrated System. 3D stacks lead to several favorable factors, such as (a) reduction in the power consumption of the system, (b) reduction in the overall form-factor of the package, and (c) significant reduction the weight of the package. There are a few challenges that must be overcome in this project, such as a commercially available microcontroller normally have an output voltage of 3.3 V to 5.5 V; however, a voltage of 7 - 8V is required to move the MEMS movable microelectrodes. To acquire higher density neural recording, more number of microelectrodes are needed. In this project, SoP Signal Generator is design to drive independently 3 moveable microelectrodes. Therefore, 12 voltage waveform are required. . However, the use of 12 signal generators is not a workable option since the system will be significantly large. This brings us to the other challenge, the limiting size of the rodent brain. Due to this factor, the SoP Signal Generator has to be deisgned to be able to fit without causing much pressure to the rodent's brain. For the first challenge, which is the limited output voltage of 3.3V on the microcontroller, the RC555 timers are used as an amplifier in addition to generating the signals. Demultiplexers have been for the next challenge, which is the need of 24 waveforms to drive 3 electrodes. For each waveform, 1 demultiplexer is used, making a total of 4 demultiplexers used in the entire system, which is a significant improvement from using 12 signal generators. The last challenge can be approached using 3D system stacking technique as mentioned above. The research aims of this project can be described as follows: (1) the testing and realization of the system part, and the designing of the system in a PCB level, (2) implementing and testing the SoP Signal Generator with the MEMS movable microelectrodes, The final outcome of this project can be used not only for neural applications, but also for more general applications that requires customized signal generations and wireless data transmission.
ContributorsTee, Zikai (Author) / Muthuswamy, Jitendran (Thesis advisor) / Sutanto, Jemmy (Committee member) / Yu, Hongyu (Committee member) / Arizona State University (Publisher)
Created2012
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Description
There is a tremendous need for wireless biological signals acquisition for the microelectrode-based neural interface to reduce the mechanical impacts introduced by wire-interconnects system. Long wire connections impede the ability to continuously record the neural signal for chronic application from the rodent's brain. Furthermore, connecting and/or disconnecting Omnetics interconnects often

There is a tremendous need for wireless biological signals acquisition for the microelectrode-based neural interface to reduce the mechanical impacts introduced by wire-interconnects system. Long wire connections impede the ability to continuously record the neural signal for chronic application from the rodent's brain. Furthermore, connecting and/or disconnecting Omnetics interconnects often introduces mechanical stress which causes blood vessel to rupture and leads to trauma to the brain tissue. Following the initial implantation trauma, glial tissue formation around the microelectrode and may possibly lead to the microelectrode signal degradation. The aim of this project is to design, develop, and test a compact and power efficient integrated system (IS) that is able to (a) wirelessly transmit triggering signal from the computer to the signal generator which supplies voltage waveforms that move the MEMS microelectrodes, (b) wirelessly transmit neural data from the brain to the external computer, and (c) provide an electrical interface for a closed loop control to continuously move the microelectrode till a proper quality of neural signal is achieved. One of the main challenges of this project is the limited data transmission rate of the commercially available wireless system to transmit 400 kbps of digitized neural signals/electrode, which include spikes, local field potential (LFP), and noise. A commercially available Bluetooth module is only capable to transmit at a total of 115 kbps data transfer rate. The approach to this challenge is to digitize the analog neural signal with a lower accuracy ADC to lower the data rate, so that is reasonable to wirelessly transfer neural data of one channel. In addition, due to the limited space and weight bearing capability to the rodent's head, a compact and power efficient integrated system is needed to reduce the packaged volume and power consumption. 3D SoP technology has been used to stack the PCBs in a 3D form-factor, proper routing designs and techniques are implemented to reduce the electrical routing resistances and the parasitic RC delay. It is expected that this 3D design will reduce the power consumption significantly in comparison to the 2D one. The progress of this project is divided into three different phases, which can be outlined as follow: a) Design, develop, and test Bluetooth wireless system to transmit the triggering signal from the computer to the signal generator. The system is designed for three moveable microelectrodes. b) Design, develop, and test Bluetooth wireless system to wirelessly transmit an amplified (200 gain) neural signal from one single electrode to an external computer. c) Design, develop, and test a closed loop control system that continuously moves a microelectrode in searching of an acceptable quality of neural spikes. The outcome of this project can be used not only for the need of neural application but also for a wider and general applications that requires customized signal generations and wireless data transmission.
ContributorsZhou, Li (Author) / Muthuswamy, Jitendran (Thesis advisor) / Sutanto, Jemmy (Thesis advisor) / Yu, Hongyu (Committee member) / Arizona State University (Publisher)
Created2012
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Description
Development of post-traumatic epilepsy (PTE) after traumatic brain injury (TBI) is a major health concern (5% - 50% of TBI cases). A significant problem in TBI management is the inability to predict which patients will develop PTE. Such prediction, followed by timely treatment, could be highly beneficial to TBI patients.

Development of post-traumatic epilepsy (PTE) after traumatic brain injury (TBI) is a major health concern (5% - 50% of TBI cases). A significant problem in TBI management is the inability to predict which patients will develop PTE. Such prediction, followed by timely treatment, could be highly beneficial to TBI patients. Six male Sprague-Dawley rats were subjected to a controlled cortical impact (CCI). A 6mm piston was pneumatically driven 3mm into the right parietal cortex with velocity of 5.5m/s. The rats were subsequently implanted with 6 intracranial electroencephalographic (EEG) electrodes. Long-term (14-week) continuous EEG recordings were conducted. Using linear (coherence) and non-linear (Lyapunov exponents) measures of EEG dynamics in conjunction with measures of network connectivity, we studied the evolution over time of the functional connectivity between brain sites in order to identify early precursors of development of epilepsy. Four of the six TBI rats developed PTE 6 to 10 weeks after the initial insult to the brain. Analysis of the continuous EEG from these rats showed a gradual increase of the connectivity between critical brain sites in terms of their EEG dynamics, starting at least 2 weeks prior to their first spontaneous seizure. In contrast, for the rats that did not develop epilepsy, connectivity levels did not change, or decreased during the whole course of the experiment across pairs of brain sites. Consistent behavior of functional connectivity changes between brain sites and the "focus" (site of impact) over time was demonstrated for coherence in three out of the four epileptic and in both non-epileptic rats, while for STLmax in all four epileptic and in both non-epileptic rats. This study provided us with the opportunity to quantitatively investigate several aspects of epileptogenesis following traumatic brain injury. Our results strongly support a network pathology that worsens with time. It is conceivable that the observed changes in spatiotemporal dynamics after an initial brain insult, and long before the development of epilepsy, could constitute a basis for predictors of epileptogenesis in TBI patients.
ContributorsTobin, Edward (Author) / Iasemidis, Leonidas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Muthuswamy, Jitendran (Committee member) / Arizona State University (Publisher)
Created2012
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Description
From time immemorial, epilepsy has persisted to be one of the greatest impediments to human life for those stricken by it. As the fourth most common neurological disorder, epilepsy causes paroxysmal electrical discharges in the brain that manifest as seizures. Seizures have the effect of debilitating patients on a physical

From time immemorial, epilepsy has persisted to be one of the greatest impediments to human life for those stricken by it. As the fourth most common neurological disorder, epilepsy causes paroxysmal electrical discharges in the brain that manifest as seizures. Seizures have the effect of debilitating patients on a physical and psychological level. Although not lethal by themselves, they can bring about total disruption in consciousness which can, in hazardous conditions, lead to fatality. Roughly 1\% of the world population suffer from epilepsy and another 30 to 50 new cases per 100,000 increase the number of affected annually. Controlling seizures in epileptic patients has therefore become a great medical and, in recent years, engineering challenge.



In this study, the conditions of human seizures are recreated in an animal model of temporal lobe epilepsy. The rodents used in this study are chemically induced to become chronically epileptic. Their Electroencephalogram (EEG) data is then recorded and analyzed to detect and predict seizures; with the ultimate goal being the control and complete suppression of seizures.



Two methods, the maximum Lyapunov exponent and the Generalized Partial Directed Coherence (GPDC), are applied on EEG data to extract meaningful information. Their effectiveness have been reported in the literature for the purpose of prediction of seizures and seizure focus localization. This study integrates these measures, through some modifications, to robustly detect seizures and separately find precursors to them and in consequence provide stimulation to the epileptic brain of rats in order to suppress seizures. Additionally open-loop stimulation with biphasic currents of various pairs of sites in differing lengths of time have helped us create control efficacy maps. While GPDC tells us about the possible location of the focus, control efficacy maps tells us how effective stimulating a certain pair of sites will be.



The results from computations performed on the data are presented and the feasibility of the control problem is discussed. The results show a new reliable means of seizure detection even in the presence of artifacts in the data. The seizure precursors provide a means of prediction, in the order of tens of minutes, prior to seizures. Closed loop stimulation experiments based on these precursors and control efficacy maps on the epileptic animals show a maximum reduction of seizure frequency by 24.26\% in one animal and reduction of length of seizures by 51.77\% in another. Thus, through this study it was shown that the implementation of the methods can ameliorate seizures in an epileptic patient. It is expected that the new knowledge and experimental techniques will provide a guide for future research in an effort to ultimately eliminate seizures in epileptic patients.
ContributorsShafique, Md Ashfaque Bin (Author) / Tsakalis, Konstantinos (Thesis advisor) / Rodriguez, Armando (Committee member) / Muthuswamy, Jitendran (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2016
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Description
Over the past fifty years, the development of sensors for biological applications has increased dramatically. This rapid growth can be attributed in part to the reduction in feature size, which the electronics industry has pioneered over the same period. The decrease in feature size has led to the production of

Over the past fifty years, the development of sensors for biological applications has increased dramatically. This rapid growth can be attributed in part to the reduction in feature size, which the electronics industry has pioneered over the same period. The decrease in feature size has led to the production of microscale sensors that are used for sensing applications, ranging from whole-body monitoring down to molecular sensing. Unfortunately, sensors are often developed without regard to how they will be integrated into biological systems. The complexities of integration are underappreciated. Integration involves more than simply making electrical connections. Interfacing microscale sensors with biological environments requires numerous considerations with respect to the creation of compatible packaging, the management of biological reagents, and the act of combining technologies with different dimensions and material properties. Recent advances in microfluidics, especially the proliferation of soft lithography manufacturing methods, have established the groundwork for creating systems that may solve many of the problems inherent to sensor-fluidic interaction. The adaptation of microelectronics manufacturing methods, such as Complementary Metal-Oxide-Semiconductor (CMOS) and Microelectromechanical Systems (MEMS) processes, allows the creation of a complete biological sensing system with integrated sensors and readout circuits. Combining these technologies is an obstacle to forming complete sensor systems. This dissertation presents new approaches for the design, fabrication, and integration of microscale sensors and microelectronics with microfluidics. The work addresses specific challenges, such as combining commercial manufacturing processes into biological systems and developing microscale sensors in these processes. This work is exemplified through a feedback-controlled microfluidic pH system to demonstrate the integration capabilities of microscale sensors for autonomous microenvironment control.
ContributorsWelch, David (Author) / Blain Christen, Jennifer (Thesis advisor) / Muthuswamy, Jitendran (Committee member) / Frakes, David (Committee member) / LaBelle, Jeffrey (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2012
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Description
Piloerection (known as goosebumps) is mediated by activation of alpha-adrenergic receptors within the sympathetic branch of the autonomic nervous system. The study of piloerection is important in multiple fields, from emotion studies to nervous system pathology. This makes piloerection particularly relevant to emotions research. Despite wide-ranging applications, current methods for

Piloerection (known as goosebumps) is mediated by activation of alpha-adrenergic receptors within the sympathetic branch of the autonomic nervous system. The study of piloerection is important in multiple fields, from emotion studies to nervous system pathology. This makes piloerection particularly relevant to emotions research. Despite wide-ranging applications, current methods for measuring piloerection are laborious and qualitative. The goal of this study is to build a wearable piloerection sensor through the use of straight-line lasers and photoresistors. The study analyzed methods of detecting and measuring goosebumps, and applied the method of laser scattering as a detection method. This device was designed and tested against a population of seven Arizona State University students. Goosebumps were elicited through conditions of cold, and video clips meant to elicit emotions of awe and sadness. Piloerection was then quantified through two controls of self-identification and camera recording, as well as the new detection method. These were then compared together, and it was found that subjective methods of determining goosebumps did not correlate well with objective measurements, but that the two objective measurements correlated well with one another. This shows that the technique of laser scattering can be used to detect goosebumps and further developments on this new detection method will be made. Moreover, the presence of uncorrelated subjective measurements further shows the need for an objective measurement of piloerection, while also bringing into question other factors that may be confused with the feeling of piloerection, such as chills or shivers. This study further reaffirmed previous studies showing a positive correlation between intense emotions.
ContributorsHemesath, Angela (Author) / Muthuswamy, Jitendran (Thesis director) / Shiota, Michelle (Lani) (Committee member) / Harrington Bioengineering Program (Contributor, Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05