Date of Graduation
Bachelor of Science
Computer Science and Computer Engineering
Committee Member/Second Reader
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.
Stolze, Sarah J., "Improving Electroencephalography-Based Imagined Speech Recognition with a Simultaneous Video Data Stream" (2016). Computer Science and Computer Engineering Undergraduate Honors Theses. 38.