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Analytical Methods for High Dimensional Physiological Sensors


Abstract This dissertation proposes a new set of analytical methods for high dimensional physiological sensors. The methodologies developed in this work were motivated by problems in learning science, but also apply to numerous disciplines where high dimensional signals are present. In the education field, more data is now available from traditional sources and there is an important need for analytical methods to translate this data into improved learning. Affecting Computing which is the study of new techniques that develop systems to recognize and model human emotions is integrating different physiological signals such as electroencephalogram (EEG) and electromyogram (EMG) to detect and model emotions which later can be used to improve these ... (more)
Created Date 2017
Contributor Lujan Moreno, Gustavo A. (Author) / Runger, George C (Advisor) / Atkinson, Robert K (Advisor) / Montgomery, Douglas (Committee member) / Villalobos, Rene (Committee member) / Arizona State University (Publisher)
Subject Industrial engineering / Statistics / Neurosciences / EEG / event-crossover / intelligent tutoring systems / machine learning / physiological / self-organizing maps
Type Doctoral Dissertation
Extent 146 pages
Language English
Copyright
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Note Doctoral Dissertation Industrial Engineering 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis