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- Creators: Sugar, Thomas
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Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Description
The sensor industry is a growing industry that has been predicted by Allied Market Research to be a multi-billion industry by 2022. One of the many key drives behind this rapid growth in the sensor industry is the increase incorporation of sensors into portable electrical devices. The value for sensor technologies are increased when the sensors are developed into innovative measuring system for application uses in the Aerospace, Defense, and Healthcare industries. While sensors are not new, their increased performance, size reduction, and decrease in cost has opened the door for innovative sensor combination for portable devices that could be worn or easily moved around. With this opportunity for further development of sensor use through concept engineering development, three concept projects for possible innovative portable devices was undertaken in this research. One project was the development of a pulse oximeter devise with fingerprint recognition. The second project was prototyping a portable Bluetooth strain gage monitoring system. The third project involved sensors being incorporated onto flexible printed circuit board (PCB) for improved comfort of wearable devices. All these systems were successfully tested in lab.
ContributorsNichols, Kevin William (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Brad (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2018
Description
Recent research and study have showed the potential of auto-parametric system in controlling stability and parametric resonance. In this project, two different designs for auto-parametrically excited mass-spring-damper systems were studied. The theoretical models were developed to describe the behavior of the systems, and simulation models were constructed to validate the analytical results. The error between simulation and theoretical results was within 2%. Both theoretical and simulation results showed that the implementation of auto-parametric system could help reduce or amplify the resonance significantly.
ContributorsLe, Thao (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Rogers, Brad (Committee member) / Arizona State University (Publisher)
Created2018
Description
Engineering is a multidisciplinary field with a variety of applications. However, since there are so many disciplines of engineering, it is often challenging to find the discipline that best suits an individual interested in engineering. Not knowing which area of engineering most aligns to one’s interests is challenging when deciding on a major and a career. With the development of the Engineering Interest Quiz (EIQ), the goal was to help individuals find the field of engineering that is most similar to their interests. Initially, an Engineering Faculty Survey (EFS) was created to gather information from engineering faculty at Arizona State University (ASU) and to determine keywords that describe each field of engineering. With this list of keywords, the EIQ was developed. Data from the EIQ compared the engineering students’ top three results for the best engineering discipline for them with their current engineering major of study. The data analysis showed that 70% of the respondents had their major listed as one of the top three results they were given and 30% of the respondents did not have their major listed. Of that 70%, 64% had their current major listed as the highest or tied for the highest percentage and 36% had their major listed as the second or third highest percentage. Furthermore, the EIQ data was compared between genders. Only 33% of the male students had their current major listed as their highest percentage, but 55% had their major as one of their top three results. Women had higher percentages with 63% listing their current major as their highest percentage and 81% listing it in the top three of their final results.
ContributorsWagner, Avery Rose (Co-author) / Lucca, Claudia (Co-author) / Taylor, David (Thesis director) / Miller, Cindy (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Hydrocephalus is a chronic medical condition characterized by the excessive accumulation of cerebrospinal fluid in the brain. It is estimated that 1-2 of every 1000 babies in the United States is born with congenital hydrocephalus, with many individuals acquiring hydrocephalus later in life through brain injury. Despite these alarming statistics, current shunts for the treatment of hydrocephalus display operational failure rates as high as 40-50% within two years following implantation. Failure of current shunts is attributed to complexity of design, external implantation, and the requirement of multiple catheters. The presented hydrogel wafer check valve avoids all the debilitating features of current shunts, relying only on the swelling of hydrogel for operation, and is designed to directly replace failed arachnoid granulations- the brain’s natural cerebrospinal fluid drainage valves. The valve was validated via bench-top (1) hydrodynamic pressure-flow response characterizations, (2) transient response analysis, and (3) overtime performance response in brain-analogous conditions. In-vitro measurements display operation in range of natural CSF draining (cracking pressure, PT ~ 1–110 mmH2O and outflow hydraulic resistance, Rh ~ 24 – 152 mmH2O/mL/min), negligible reverse flow leakages (flow, QO > -10 µL/min), and demonstrate the valve’s operational reproducibility of this new valve as an implantable treatment.
ContributorsAmjad, Usamma Muhammad (Author) / Chae, Junseok (Thesis director) / Appel, Jennie (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Depletion of fossil fuel resources has led to the investigation of alternate feedstocks for and methods of chemical synthesis, in particular the use of E. coli biocatalysts to produce fine commodity chemicals from renewable glucose sources. Production of phenol, 2-phenylethanol, and styrene was investigated, in particular the limitation in yield and accumulation that results from high product toxicity. This paper examines two methods of product toxicity mitigation: the use of efflux pumps and the separation of pathways which produce less toxic intermediates. A library of 43 efflux pumps from various organisms were screened for their potential to confer resistance to phenol, 2-phenylethanol, and styrene on an E. coli host. A pump sourced from P. putida was found to allow for increased host growth in the presence of styrene as compared to a cell with no efflux pump. The separation of styrene producing pathway was also investigated. Cells capable of performing the first and latter halves of the synthesis were allowed to grow separately and later combined in order to capitalize on the relatively lower toxicity of the intermediate, trans-cinnamate. The styrene production and yield from this separated set of cultures was compared to that resulting from the growth of cells containing the full set of styrene synthesis genes. Results from this experiment were inconclusive.
ContributorsLallmamode, Noor Atiya Jabeen (Author) / Nielsen, David (Thesis director) / Cadillo-Quiroz, Hinsby (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor) / School of Life Sciences (Contributor)
Created2015-05
Description
The goals of the styrene oxide adsorption experiments were to develop reliable isotherms of styrene oxide onto Dowex Optipore L-493 resin and onto mesoporous carbon adsorbents, in addition to determining the ideal conditions for styrene oxide production from E. coli. Adsorption is an effective means of separation used in industry to separate compounds, often organics from air and water. Styrene oxide adsorption runs without E. coli were conducted at concentrations ranging from 0.15 to 3.00 g/L with resin masses ranging from 0.1 to 0.5 g of Dowex Optipore L-493 and 0.5 to 0.75 g of mesoporous carbon adsorbent. Runs were conducted on a shake plate operating at 80 rpm for 24 hours at ambient temperature. Isotherms were developed from the results and then adsorption experiments with E. coli and L-493 were performed. Runs were conducted at glucose concentrations ranging from 20-40 g/L and resin masses of 0.100 g to 0.800 g. Samples were incubated for 72 hours and styrene oxide production was measured using an HPLC device. Specific loading values reached up to 0.356 g/g for runs without E. coli and nearly 0.003 g of styrene oxide was adsorbed by L-493 during runs with E. coli. Styrene oxide production was most effective at low resin masses and medium glucose concentrations when produced by E. coli.
ContributorsHsu, Joshua (Co-author) / Oremland, Zachary (Co-author) / Nielsen, David (Thesis director) / Staggs, Kyle (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor) / School of Sustainability (Contributor)
Created2014-05
Description
The inability of a single strain of bacteria to simultaneously and completely consume multiple sugars, such as glucose and xylose, hinder industrial microbial processes for ethanol and lactate production. To overcome this limitation, I am engineering an E. coli co-culture system consisting of two ‘specialists'. One has the ability to only consume xylose and the other only glucose. This allows for co-utilization of lignocellulose-derived sugars so both sugars are completely consumed, residence time is reduced and lactate and ethanol titers are maximized.
ContributorsAyla, Zeynep Ece (Author) / Nielsen, David (Thesis director) / Flores, Andrew (Committee member) / Chemical Engineering Program (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The objective of this research study is to assess the effectiveness of a poster-based messaging campaign and engineering-based activities for middle school and high school students to encourage students to explore and to pursue chemical engineering. Additionally, presentations are incorporated into both methods to provide context and improve understanding of the presented poster material or activity. Pre-assessments and post-assessments are the quantitative method of measuring effectiveness. For the poster campaign, ASU juniors and seniors participated in the poster campaign by producing socially relevant messages about their research or aspirations to address relevant chemical engineering problems. For the engineering-based activity, high school students participated in an Ira A. Fulton Schools of Engineering program "Young Engineers Shape the World" in which the students participated in six-hour event learning about four engineering disciplines, and the chemical engineering presentation and activity was conducted in one of the sessions. Pre-assessments were given at the beginning of the event, and the post-assessments were provided towards the end of the event. This honors thesis project will analyze the collected data.
ContributorsBueno, Daniel Tolentino (Author) / Ganesh, Tirupalavanam (Thesis director) / Parker, Hope (Committee member) / Chemical Engineering Program (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Many industries require workers in warehouse and stockroom environments to perform frequent lifting tasks. Over time these repeated tasks can lead to excess strain on the worker's body and reduced productivity. This project seeks to develop an exoskeletal wrist fixture to be used in conjunction with a powered exoskeleton arm to aid workers performing box lifting types of tasks. Existing products aimed at improving worker comfort and productivity typically employ either fully powered exoskeleton suits or utilize minimally powered spring arms and/or fixtures. These designs either reduce stress to the user's body through powered arms and grippers operated via handheld controls which have limited functionality, or they use a more minimal setup that reduces some load, but exposes the user's hands and wrists to injury by directing support to the forearm. The design proposed here seeks to strike a balance between size, weight, and power requirements and also proposes a novel wrist exoskeleton design which minimizes stress on the user's wrists by directly interfacing with the object to be picked up. The design of the wrist exoskeleton was approached through initially selecting degrees of freedom and a ROM (range of motion) to accommodate. Feel and functionality were improved through an iterative prototyping process which yielded two primary designs. A novel "clip-in" method was proposed to allow the user to easily attach and detach from the exoskeleton. Designs utilized a contact surface intended to be used with dry fibrillary adhesives to maximize exoskeleton grip. Two final designs, which used two pivots in opposite kinematic order, were constructed and tested to determine the best kinematic layout. The best design had two prototypes created to be worn with passive test arms that attached to the user though a specially designed belt.
ContributorsGreason, Kenneth Berend (Author) / Sugar, Thomas (Thesis director) / Holgate, Matthew (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12