Matching Items (20)
Description
The human hand is a complex biological system. Humans have evolved a unique ability to use the hand for a wide range of tasks, including activities of daily living such as successfully grasping and manipulating objects, i.e., lifting a cup of coffee without spilling. Despite the ubiquitous nature of hand use in everyday activities involving object manipulations, there is currently an incomplete understanding of the cortical sensorimotor mechanisms underlying this important behavior. One critical aspect of natural object grasping is the coordination of where the fingers make contact with an object and how much force is applied following contact. Such force-to-position modulation is critical for successful manipulation. However, the neural mechanisms underlying these motor processes remain less understood, as previous experiments have utilized protocols with fixed contact points which likely rely on different neural mechanisms from those involved in grasping at unconstrained contacts. To address this gap in the motor neuroscience field, transcranial magnetic stimulation (TMS) and electroencephalography (EEG) were used to investigate the role of primary motor cortex (M1), as well as other important cortical regions in the grasping network, during the planning and execution of object grasping and manipulation. The results of virtual lesions induced by TMS and EEG revealed grasp context-specific cortical mechanisms underlying digit force-to-position coordination, as well as the spatial and temporal dynamics of cortical activity during planning and execution. Together, the present findings provide the foundation for a novel framework accounting for how the central nervous system controls dexterous manipulation. This new knowledge can potentially benefit research in neuroprosthetics and improve the efficacy of neurorehabilitation techniques for patients affected by sensorimotor impairments.
ContributorsMcGurrin, Patrick M (Author) / Santello, Marco (Thesis advisor) / Helms-Tillery, Steve (Committee member) / Kleim, Jeff (Committee member) / Davare, Marco (Committee member) / Arizona State University (Publisher)
Created2017
Description
It is unknown which regions of the brain are most or least active for golfers during a peak performance state (Flow State or "The Zone") on the putting green. To address this issue, electroencephalographic (EEG) recordings were taken on 10 elite golfers while they performed a putting drill consisting of hitting nine putts spaced uniformly around a hole each five feet away. Data was collected at three time periods, before, during and after the putt. Galvanic Skin Response (GSR) measurements were also recorded on each subject. Three of the subjects performed a visualization of the same putting drill and their brain waves and GSR were recorded and then compared with their actual performance of the drill. EEG data in the Theta (4 \u2014 7 Hz) bandwidth and Alpha (7 \u2014 13 Hz) bandwidth in 11 different locations across the head were analyzed. Relative power spectrum was used to quantify the data. From the results, it was found that there is a higher magnitude of power in both the theta and alpha bandwidths for a missed putt in comparison to a made putt (p<0.05). It was also found that there is a higher average power in the right hemisphere for made putts. There was not a higher power in the occipital region of the brain nor was there a lower power level in the frontal cortical region during made putts. The hypothesis that there would be a difference between the means of the power level in performance compared to visualization techniques was also supported.
ContributorsCarpenter, Andrea (Co-author) / Hool, Nicholas (Co-author) / Muthuswamy, Jitendran (Thesis director) / Crews, Debbie (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Electroencephalogram (EEG) used simultaneously with video monitoring can record detailed patient physiology during a seizure to aid diagnosis. However, current patient monitoring systems typically require a patient to stay in view of a fixed camera limiting their freedom of movement. The goal of this project is to design an automatic patient monitoring system with software to track patient movement in order to increase a patient's mobility. This report discusses the impact of an automatic patient monitoring system and the design steps used to create and test a functional prototype.
ContributorsBui, Robert Truong (Author) / Frakes, David (Thesis director) / Helms Tillery, Stephen (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2014-05
Description
Electromyography (EMG) and Electroencephalography (EEG) are techniques used to detect electrical activity produced by the human body. EMG detects electrical activity in the skeletal muscles, while EEG detects electrical activity from the scalp. The purpose of this study is to capture different types of EMG and EEG signals and to determine if the signals can be distinguished between each other and processed into output signals to trigger events in prosthetics. Results from the study suggest that the PSD estimates can be used to compare signals that have significant differences such as the wrist, scalp, and fingers, but it cannot fully distinguish between signals that are closely related, such as two different fingers. The signals that were identified were able to be translated into the physical output simulated on the Arduino circuit.
ContributorsJanis, William Edward (Author) / LaBelle, Jeffrey (Thesis director) / Santello, Marco (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-12
Description
Intracranial pressure is an important parameter to monitor, and elevated intracranial pressure can be life threatening. Elevated intracranial pressure is indicative of distress in the brain attributed by conditions such as aneurysm, traumatic brain injury, brain tumor, hydrocephalus, stroke, or meningitis.
Electrocorticography (ECoG) recordings are invaluable in understanding epilepsy and detecting seizure zones. However, ECoG electrodes cause a foreign body mass effect, swelling, and pneumocephaly, which results in elevation of intracranial pressure (ICP). Thus, the aim of this work is to design an intracranial pressure monitoring system that could augment ECoG electrodes.
A minimally invasive, low-cost epidural intracranial pressure monitoring system is developed for this purpose, using a commercial pressure transducer available for biomedical applications. The system is composed of a pressure transducer, sensing cup, electronics, and data acquisition system. The pressure transducer is a microelectromechanical system (MEMS)-based die that works on piezoresistive phenomenon with dielectric isolation for direct contact with fluids.
The developed system was bench tested and verified in an animal model to confirm the efficacy of the system for intracranial pressure monitoring. The system has a 0.1 mmHg accuracy and a 2% error for the 0-10 mmHg range, with resolution of 0.01 mmHg. This system serves as a minimally invasive (2 mm burr hole) epidural ICP monitor, which could augment existing ECoG electrode arrays, to simultaneously measure intracranial pressure along with the neural signals.
This device could also be employed with brain implants that causes elevation in ICP due to tissue - implant interaction often leading to edema. This research explores the concept and feasibility for integrating the sensing component directly on to the ECoG electrode arrays.
Electrocorticography (ECoG) recordings are invaluable in understanding epilepsy and detecting seizure zones. However, ECoG electrodes cause a foreign body mass effect, swelling, and pneumocephaly, which results in elevation of intracranial pressure (ICP). Thus, the aim of this work is to design an intracranial pressure monitoring system that could augment ECoG electrodes.
A minimally invasive, low-cost epidural intracranial pressure monitoring system is developed for this purpose, using a commercial pressure transducer available for biomedical applications. The system is composed of a pressure transducer, sensing cup, electronics, and data acquisition system. The pressure transducer is a microelectromechanical system (MEMS)-based die that works on piezoresistive phenomenon with dielectric isolation for direct contact with fluids.
The developed system was bench tested and verified in an animal model to confirm the efficacy of the system for intracranial pressure monitoring. The system has a 0.1 mmHg accuracy and a 2% error for the 0-10 mmHg range, with resolution of 0.01 mmHg. This system serves as a minimally invasive (2 mm burr hole) epidural ICP monitor, which could augment existing ECoG electrode arrays, to simultaneously measure intracranial pressure along with the neural signals.
This device could also be employed with brain implants that causes elevation in ICP due to tissue - implant interaction often leading to edema. This research explores the concept and feasibility for integrating the sensing component directly on to the ECoG electrode arrays.
ContributorsSampath Kumaran, Ranjani (Author) / Christen, Jennifer Blain (Thesis advisor) / Tillery, Stephen Helms (Committee member) / Greger, Bradley (Committee member) / Arizona State University (Publisher)
Created2015
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 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.
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
Description
The cocktail party effect describes the brain’s natural ability to attend to a specific voice or audio source in a crowded room. Researchers have recently attempted to recreate this ability in hearing aid design using brain signals from invasive electrocorticography electrodes. The present study aims to find neural signatures of auditory attention to achieve this same goal with noninvasive electroencephalographic (EEG) methods. Five human participants participated in an auditory attention task. Participants listened to a series of four syllables followed by a fifth syllable (probe syllable). Participants were instructed to indicate whether or not the probe syllable was one of the four syllables played immediately before the probe syllable. Trials of this task were separated into conditions of playing the syllables in silence (Signal) and in background noise (Signal With Noise), and both behavioral and EEG data were recorded. EEG signals were analyzed with event-related potential and time-frequency analysis methods. The behavioral data indicated that participants performed better on the task during the “Signal” condition, which aligns with the challenges demonstrated in the cocktail party effect. The EEG analysis showed that the alpha band’s (9-13 Hz) inter-trial coherence could potentially indicate characteristics of the attended speech signal. These preliminary results suggest that EEG time-frequency analysis has the potential to reveal the neural signatures of auditory attention, which may allow for the design of a noninvasive, EEG-based hearing aid.
ContributorsLaBine, Alyssa (Author) / Daliri, Ayoub (Thesis director) / Chao, Saraching (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor) / Harrington Bioengineering Program (Contributor)
Created2023-05
Description
More than a century of research has investigated the etiology of dyslexia, coalescing around ‘phonological awareness’ – the ability to recognize and manipulate phonemes – as a trait typically deficient in reading disorders. Meanwhile, the last few decades of research in neuroscience have highlighted the brain as a predictive organ, which subliminally calibrates sensory expectations according to experience. Do the brains of adults with dyslexia respond differently than those of matched controls to expected tones and unexpected omissions? While auditory oddball paradigms have previously been used to study dyslexia, these studies often interpret group differences to indicate deficit auditory discrimination rather than deficit auditory prediction. The current study takes a step toward fusing theories of predictive coding and dyslexia, finding that event-related potentials related to auditory prediction are attenuated in adults with dyslexia compared with typical controls. It further suggests that understanding dyslexia, and perhaps other psychiatric disorders, in terms of contributory neural systems will elucidate shared and distinct etiologies.
ContributorsBennett, Augustin (Author) / Peter, Beate (Thesis advisor) / Daliri, Ayoub (Committee member) / Goldinger, Stephen (Committee member) / Arizona State University (Publisher)
Created2023
Description
Beneath the epidermis, the human body contains a vibrant and complex ecology of interwoven rhythms such the heartbeat, the breath, the division of cells, and complex brain activity. By repurposing emergent medical technology into real-time gestural sound controllers of electronic musical instruments, experimental musicians in the 1960s and 1970s – including David Rosenboom – began to realize the expressive potential of these biological sounds. Composers experimented with breath and heartbeat. They also used electroencephalography (EEG) sensors, which register various types of brain waves. Instead of using the sound of brain waves in fixed-media pieces, many composers took diverse approaches to the challenge of presenting this in live performance. Their performance practices suggest different notions of embodiment, a relationship in this music which has not been discussed in detail.
Rosenboom reflects extensively on this performance practice. He supports his EEG research with theory about the practice of biofeedback. Rosenboom’s work with EEG sensors spans several decades and continue today, which has allowed him to make use of advancing sensing and computing technologies. For instance, in his 1976 On Being Invisible, the culmination of his work with EEG, he makes use of analyzed EEG data to drive a co-improvising musical system.
In this thesis, I parse different notions of embodiment in the performance of EEG music. Through a critical analysis of examples from the discourse surrounding EEG music in its early years, I show that cultural perception of EEG sonification points to imaginative speculations about the practice’s potentials; these fantasies have fascinating ramifications on the role of the body in this music’s performance. Juxtaposing these with Rosenboom, I contend that he cultivated an embodied performance practice of the EEG. To show how this might be manifest in performance, I consider two recordings of On Being Invisible.
As few musicologists have investigated this particular strain of musical experimentalism, I hope to contextualize biofeedback musicianship by offering an embodied reading of this milestone work for EEG.
Rosenboom reflects extensively on this performance practice. He supports his EEG research with theory about the practice of biofeedback. Rosenboom’s work with EEG sensors spans several decades and continue today, which has allowed him to make use of advancing sensing and computing technologies. For instance, in his 1976 On Being Invisible, the culmination of his work with EEG, he makes use of analyzed EEG data to drive a co-improvising musical system.
In this thesis, I parse different notions of embodiment in the performance of EEG music. Through a critical analysis of examples from the discourse surrounding EEG music in its early years, I show that cultural perception of EEG sonification points to imaginative speculations about the practice’s potentials; these fantasies have fascinating ramifications on the role of the body in this music’s performance. Juxtaposing these with Rosenboom, I contend that he cultivated an embodied performance practice of the EEG. To show how this might be manifest in performance, I consider two recordings of On Being Invisible.
As few musicologists have investigated this particular strain of musical experimentalism, I hope to contextualize biofeedback musicianship by offering an embodied reading of this milestone work for EEG.
ContributorsJohnson, Garrett Laroy (Author) / Xin Wei, Sha (Thesis advisor) / Ingalls, Todd (Committee member) / Suzuki, Kotoka (Committee member) / Tobias, Evan (Committee member) / Arizona State University (Publisher)
Created2015
Description
Aortic aneurysms and dissections are life threatening conditions addressed by replacing damaged sections of the aorta. Blood circulation must be halted to facilitate repairs. Ischemia places the body, especially the brain, at risk of damage. Deep hypothermia circulatory arrest (DHCA) is employed to protect patients and provide time for surgeons to complete repairs on the basis that reducing body temperature suppresses the metabolic rate. Supplementary surgical techniques can be employed to reinforce the brain's protection and increase the duration circulation can be suspended. Even then, protection is not completely guaranteed though. A medical condition that can arise early in recovery is postoperative delirium, which is correlated with poor long term outcome. This study develops a methodology to intraoperatively monitor neurophysiology through electroencephalography (EEG) and anticipate postoperative delirium. The earliest opportunity to detect occurrences of complications through EEG is immediately following DHCA during warming. The first observable electrophysiological activity after being completely suppressed is a phenomenon known as burst suppression, which is related to the brain's metabolic state and recovery of nominal neurological function. A metric termed burst suppression duty cycle (BSDC) is developed to characterize the changing electrophysiological dynamics. Predictions of postoperative delirium incidences are made by identifying deviations in the way these dynamics evolve. Sixteen cases are examined in this study. Accurate predictions can be made, where on average 89.74% of cases are correctly classified when burst suppression concludes and 78.10% when burst suppression begins. The best case receiver operating characteristic curve has an area under its convex hull of 0.8988, whereas the worst case area under the hull is 0.7889. These results demonstrate the feasibility of monitoring BSDC to anticipate postoperative delirium during burst suppression. They also motivate a further analysis on identifying footprints of causal mechanisms of neural injury within BSDC. Being able to raise warning signs of postoperative delirium early provides an opportunity to intervene and potentially avert neurological complications. Doing so would improve the success rate and quality of life after surgery.
ContributorsMa, Owen (Author) / Bliss, Daniel W (Thesis advisor) / Berisha, Visar (Committee member) / Kosut, Oliver (Committee member) / Brewer, Gene (Committee member) / Arizona State University (Publisher)
Created2020