Using Machine Learning Techniques to Model Encoder/Decoder Pair for Non-invasive Electroencephalographic Wireless Signal Transmission

Aug 3, 2026

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Thesis investigates using machine learning to model an encoder/decoder pair for non-invasive electroencephalographic wireless signal transmission. The work frames an end-to-end approach that targets reliable data transfer while handling the constraints of EEG sensing and wireless communication. The study organizes a research plan, dataset and supporting resources, then conducts data analysis to compare experimental approaches. Results and proof-of-concept findings guide the significance of the project for practical EEG-based monitoring.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Introduction

Literature Review

Methodology

Research Design

Proof of Concept

Dataset and Resources

Data Analysis

Research Plan

Significance of Project

Research Results

First Approach

Second Approach

Kennesaw State University DigitalCommons@Kennesaw State University | Master of Science in Computer Science Theses | Department of Computer Science | | --- | --- | | Summer 7-15-2023
Using Machine Learning Techniques to Model Encoder/Decoder Pair for Non-invasive Electroencephalographic Wireless Signal Transmission
Ernst Fanfan
Kennesaw State University
Follow this and additional works at: [https://digitalcommons.kennesaw.edu/cs_etd](https://digitalcommons.kennesaw.edu/cs_etd)
Part of the Artificial Intelligence and Robotics Commons, and the Management Information Systems Commons | | Recommended Citation Fanfan, Ernst, "Using Machine Learning Techniques to Model Encoder/Decoder Pair for Non-invasive Electroencephalographic Wireless Signal Transmission" (2023) . Master of Science in Computer Science Theses. 57. [https://digitalcommons.kennesaw.edu/cs_etd/57](https://digitalcommons.kennesaw.edu/cs_etd/57) This Thesis is brought to you for free and open access by the Department of Computer Science at DigitalCommons@Kennesaw State University. It has been accepted for inclusion in Master of Science in Computer Science Theses by an authorized administrator of DigitalCommons@Kennesaw State University. For more information, please [contact digitalcommons@kennesaw.edu](contact digitalcommons@kennesaw.edu). Using Machine Learning Techniques to Model Encoder/Decoder Pair for Non-invasive Electroencephalographic Wireless Signal Transmission By Ernst Richard Fanfan Thesis Submitted to the Faculty of Kennesaw State University in Partial Fulfillment of the Requirements for a Master of Science in Computer Science in the College of Computing and Software Engineering Computer Science Department Kennesaw State University, Kennesaw, GA Dedication I wish to dedicate this thesis to the following: • My mother, Marie Michelle, you are the source of my perseverance and dedication. • My father, Ernst Murat, you are the source of my strength and attention to detail. • My girls, Isabelle and Annalisse, you are the force that makes me strive for a better tomorrow. I miss you both. • My love, Israa, thank you for showing me a dream come true. • My best friend, Imad, I would not be here ifit was not for you. • Major Westley Hinckley, thank you for being my friend. I hope you find peace in the afterlife. • My very first mentor, Wayne Thomas, without you, I would never know true work ethic. Thank you. You all touched my life and helped me forge the man I am today. Acknowledgments I am eternally grateful to the four professors who guided my educational career. Dr. Adriane Randolph, thank you for seeing the whole of me. This is a concept I intend to employ in my journey. Thank you for the push when I needed it and for showing me how to make my fantasy into reality. Dr. Summit Chakravarty, working with you has been a joy, and your insight is greatly appreciated. I can safely say that without you, this paper would not exist. Dr. Kun Suo was the very first professor I worked for. You open my eyes to this world I am now striving to join. Thankyou for trusting me. Dr. Jose Garrido, thank you for being patient with me and entertaining my crazy ideas. Without you, Dr. G., this committee would not exist, and I would have never considered the thesis path. To all, you are the spark that ignites the passion and curiosity that drive the world of academia and research. Table of Content Dedication ............................................................................................................................. ii Acknowledgments ....................................................................... Error! Bookmark not defined. Table of Content ................................................................................................................... iv Table Of figures .............................................................................................................

FAQs

What problem does the thesis address?

The thesis focuses on modeling an encoder/decoder pair to enable non-invasive electroencephalographic (EEG) wireless signal transmission.

How is the research structured?

It follows a research design process that includes a proof of concept, a dataset and resources section, data analysis, and a staged research plan.

What are the main outcome directions?

The thesis reports research results through first and second approaches, using the collected data analysis to evaluate progress and significance of the project.

Aug 3, 2026

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