• Project Title: Leveraging AI to Create an EEG-based Smart Phone Medical Diagnosis Application

  • BASIS Advisor: Mr. Linhares

  • Internship Location: Texas State University

  • Onsite Mentor: Dr. Jelena Tesic

If the human brain were a computer, it would process approximately 38 thousand trillion operations per second, and as a result, past efforts to use brain data to detect seizures have required manual, live human monitor of brain waves. In this research paper, I will work with a lab at Texas State University to investigate mathematically modeling brain waves in the form of EEG data as a tool to recognize episodes of brain-related conditions, in particular Parkinson’s, epilepsy, and potentially autism. With the advent of deep learning, more computational power, and stronger datasets, the lab hopes to eliminate the human component of these brain conditions by training a strong predictor that can be scaled to an iOS App for daily use. Furthermore, the smartphone app and predictor will aim replace and eliminate the human component of seizure recognition and detection, as humans are prone to error. In this paper, I will investigate RNN, time series modeling, and other statistics focused on wave data. Ultimately, my research will use deep learning, statistics and mathematical modeling to create effective predictors that make use of brain waves.