Term of Award
Spring 2024
Degree Name
Master of Science, Electrical Engineering
Document Type and Release Option
Thesis (restricted to Georgia Southern)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Electrical and Computer Engineering
Committee Chair
Dr. Rocio Alba-Flores
Committee Member 1
Dr. Mohammad Ahad
Committee Member 2
Dr. Fernando Rios
Abstract
Sinusoidal steady state visually evoked potential signals, or SSVEP signals, are a type of brain signal generated through the userโs prolonged exposure to flickering visual stimuli. The canonical correlation analysis(CCA) method is a means of classifying this type of signal by comparing the frequency components of the recorded SSVEP signal to a generated reference signal. This method can be further improved through the use of a filter bank to divide the signal into multiple sub bands before performing analysis and combining the results to determine the frequency classification. The experiment performed in this paper tested five overall parameters to determine which settings would optimize performance for four frequency classifications at 8 Hz, 9 Hz, 10 Hz, and 11 Hz. The parameters tested included: number of reference harmonics, weights a and b, number of sub bands in the filter bank, and the stopband frequencies of the filter bank designs. The highest overall classification accuracy of 98.69 percent was found under four reference harmonics in a filter bank comprised of equally spaced start and stop bands, with four filtered sub bands, and a weight combination of ๐ = 2, ๐ = 0.75.
Recommended Citation
Ferrara, Nicholas, "Optimization of Filter Bank Canonical Correlation Analysis for Ssvep Based Brain Computer Interface" (2024). Electronic Theses and Dissertations. 2784.
https://digitalcommons.georgiasouthern.edu/etd/2784
Research Data and Supplementary Material
No
