Improving Electroencephalography-Based Imagined Speech Recognition with a Simultaneous Video Data Stream

Author

Sarah J. StolzeFollow

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