Graduation Year
2023
Document Type
Thesis
Degree
M.S.B.E.
Degree Name
MS in Biomedical Engineering (M.S.B.E.)
Degree Granting Department
Engineering
Major Professor
Stephen E. Saddow, Ph.D.
Committee Member
Mark Jaroszeski, Ph.D.
Committee Member
Robert Frisina, Jr., Ph.D.
Keywords
Brain Machine Interface, Classification, Electroencephalogram, MATLAB, Motor Imagery
Abstract
To date, many challenges have been reported in the development of neuroprosthetic hands using EEG and neural signals. In this study, we report the results of a literature review on Brain Computer Interface (BCI) technology, an investigation of estimation methods using applications in MATLAB, and the results of Electroencephalography (EEG) classification to assist in the development of neural prosthetic hands using biological signals such as EEG. Confusion Matrix was created using Motor Imagery (MI) data as the predictive value, and the average accuracy of more than 90% was obtained for the K-Nearest Neighbor (KNN) and decision tree method. The results were evaluated with 5-Cross Validation and discussed with Confusion Matrix. The results suggest that misclassification may occur in multiclass classification with more than two types of directions including bimanual movements, as in this study, because the MI of dominant and non-dominant hands are significantly different.
Scholar Commons Citation
Yamauchi, Keigo, "EEG Classifier Validation Methods for Neuroprosthetic Hand Development" (2023). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/9947
