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Signal Processing and Machine Learning Techniques Towards Various Real-World Applications

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Machine learning (ML) has played an important role in several modern technological innovations and has become an important tool for researchers in various fields of interest. Besides engineering, ML techniques have started to spread across various departments of study, like health-care, medicine, diagnostics, social science, finance, economics etc. These techniques…

Machine learning (ML) has played an important role in several modern technological innovations and has become an important tool for researchers in various fields of interest. Besides engineering, ML techniques have started to spread across various departments of study, like health-care, medicine, diagnostics, social science, finance, economics etc. These techniques require data to train the algorithms and model a complex system and make predictions based on that model. Due to development of sophisticated sensors it has become easier to collect large volumes of data which is used to make necessary hypotheses using ML. The promising results obtained using ML have opened up new opportunities of research across various departments and this dissertation is a manifestation of it. Here, some unique studies have been presented, from which valuable inference have been drawn for a real-world complex system. Each study has its own unique sets of motivation and relevance to the real world. An ensemble of signal processing (SP) and ML techniques have been explored in each study. This dissertation provides the detailed systematic approach and discusses the results achieved in each study. Valuable inferences drawn from each study play a vital role in areas of science and technology, and it is worth further investigation. This dissertation also provides a set of useful SP and ML tools for researchers in various fields of interest.
ContributorsDutta, Arindam (Author) / Bliss, Daniel W (Thesis advisor) / Berisha, Visar (Committee member) / Richmond, Christ (Committee member) / Corman, Steven (Committee member) / Arizona State University (Publisher)
Created2018
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Electroencephalography Feature Extraction of Neural Stimuli

Description
Many mysteries still surround brain function, and yet greater understanding of it is vital to advancing scientific research. Studies on the brain in particular play a huge role in the medical field as analysis can lead to proper diagnosis of patients and to anticipatory treatments. The objective of this research…

Many mysteries still surround brain function, and yet greater understanding of it is vital to advancing scientific research. Studies on the brain in particular play a huge role in the medical field as analysis can lead to proper diagnosis of patients and to anticipatory treatments. The objective of this research was to apply signal processing techniques on electroencephalogram (EEG) data in order to extract features for which to quantify an activity performed or a response to stimuli. The responses by the brain were shown in eigenspectrum plots in combination with time-frequency plots for each of the sensors to provide both spatial and temporal frequency analysis. Through this method, it was revealed how the brain responds to various stimuli not typically used in current research. Future applications might include testing similar stimuli on patients with neurological diseases to gain further insight into their condition.
ContributorsJackson, Matthew Joseph (Author) / Bliss, Daniel (Thesis director) / Berisha, Visar (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05