| Abstract |
My project aims to develop an innovative technology utilizing high-density EEG data to predict epileptic seizures. By integrating circadian rhythms and glial cell activity, along with advanced algorithms like Matching Pursuit and Support Vector Machines, I extract nonlinear features from EEG signals. This facilitates accurate classification of preictal and interictal states, providing valuable insights for seizure prediction. My goal is to empower individuals with epilepsy to manage their condition more effectively, enhancing their quality of life. |
