Term of Award
Spring 2022
Degree Name
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Mathematical Sciences
Committee Chair
Ionut Iacob
Committee Member 1
Yan Wu
Committee Member 2
Felix Hamza-Lup
Abstract
The study of elecroencephalograms (EEGs) has gained enormous interest in the last decade with the increase of computational power and availability of EEG signals collected from various human activities or produced during medical tests. The applicability of analyzing EEG signals ranges from helping impaired people communicate or move (using appropriate medical equipment) to understanding people's feelings and detecting diseases.
We proposed new methodology and models for analyzing and classifying EEG signals collected from individuals observing visual stimuli. Our models rely on powerful Long-Short Term Memory (LSTM) Neural Network models, which are currently the state of the art models for performing time series classifications.
OCLC Number
1365379615
Catalog Permalink
https://galileo-georgiasouthern.primo.exlibrisgroup.com/permalink/01GALI_GASOUTH/1r4bu70/alma9916470447602950
Recommended Citation
Orgeron, James A., "EEG Signals Classification Using LSTM-Based Models and Majority Logic" (2022). Electronic Theses and Dissertations. 2391.
https://digitalcommons.georgiasouthern.edu/etd/2391
Research Data and Supplementary Material
No
Included in
Artificial Intelligence and Robotics Commons, Data Science Commons, Other Applied Mathematics Commons
