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Published Honors Thesis
Theoretical Analysis of CNNs for Automatic Seizure Detection in EEG Signals
Published in UCF STARS Digital Repository
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Small, J. T. (2025). Theoretical Analysis of CNNs for Automatic Seizure Detection in EEG Signals. Honors Undergraduate Theses, Paper 462. University of Central Florida, Burnett Honors College.
Abstract:
Epilepsy is a common brain disorder where neurons in the brain rapidly fire, causing recurring seizures. The brain activity during a seizure can be detected by electroencephalogram (EEG) signals; however, this process is not only labor-intensive and time-consuming but is also subject to inter-rater variability, with a study showing only moderate agreement when diagnosing patients, even among experts. Convolutional Neural Networks (CNNs) are often proposed to detect seizures automatically, achieving high performance. The focus on performance comes at a cost of losing interpretability, leaving the model as effective but seen as a 'black box'. This thesis confronts the interpretability knowledge gap by conducting a theoretical analysis of a 1D CNN trained on the Bonn EEG Dataset. The analysis reveals how exactly the model learns, showing that the first convolutional layer develops specific filters. Across most classification tasks, the model learned to focus on the approximately 22 Hz beta-wave band as a key neurophysiological feature. Furthermore, the model's stability was quantified using the Lipschitz bound. This work successfully bridges the gap between high-performance metrics and theoretical understanding, providing a framework for interpreting CNN based seizure detection.
Keywords: Epilepsy; CNN; Deep Learning; Data Science; Seizure; EEG
Presentations
Small, J. T. (September 2025). "Theoretical Analysis of CNNs for Automatic Seizure Detection in EEG Signals." Poster presentation at Burnett Honors College Family Weekend, University of Central Florida, Orlando, FL.
Abstract:
Presented findings from undergraduate honors thesis to faculty, students, and families. Demonstrated CNN architecture, Lipschitz stability analysis, and frequency domain interpretation of learned features. Poster included proposed CNN architecture, hypothesis, and theoretical interpretation.
Citation Information
If you use this work in your research, please cite:
@thesis{small2025cnn,
author = {Small, Jackson T.},
title = {Theoretical Analysis of CNNs for Automatic Seizure Detection in EEG Signals},
school = {University of Central Florida},
year = {2025},
type = {Honors Undergraduate Theses},
number = {462},
url = {https://stars.library.ucf.edu/hut2024/462}
}