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Eeg Classifier Validation Methods For Neuroprosthetic Hand Development, Keigo Yamauchi
Eeg Classifier Validation Methods For Neuroprosthetic Hand Development, Keigo Yamauchi
USF Tampa Graduate Theses and Dissertations
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 …