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

Creative Commons License
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 userโ€™s 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‘Ž = 2, ๐‘ = 0.75.

OCLC Number

1434254269

Research Data and Supplementary Material

No

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