Date of Graduation
5-2016
Document Type
Thesis
Degree Name
Bachelor of Science
Degree Level
Undergraduate
Department
Computer Science and Computer Engineering
Advisor/Mentor
Gashler, Michael
Committee Member/Reader
Patitz, Matthew
Committee Member/Second Reader
Wu, Xintao
Abstract
Electroencephalography (EEG) devices offer a non-invasive mechanism for implementing imagined speech recognition, the process of estimating words or commands that a person expresses only in thought. However, existing methods can only achieve limited predictive accuracy with very small vocabularies; and therefore are not yet sufficient to enable fluid communication between humans and machines. This project proposes a new method for improving the ability of a classifying algorithm to recognize imagined speech recognition, by collecting and analyzing a large dataset of simultaneous EEG and video data streams. The results from this project suggest confirmation that complementing high-dimensional EEG data with similarly high-dimensional video data enhances a classifier’s ability to extract features from an EEG stream and facilitate imagined speech recognition.
Citation
Stolze, S. J. (2016). Improving Electroencephalography-Based Imagined Speech Recognition with a Simultaneous Video Data Stream. Computer Science and Computer Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/csceuht/38
Included in
Artificial Intelligence and Robotics Commons, Graphics and Human Computer Interfaces Commons, Other Computer Sciences Commons
