Epilepsy is one of the most common neurological disorders with around 50 million people affected worldwide. Epilepsy causes sudden abnormal, excessive electrical discharges among neurons in the brain, leading to dysfunction and symptoms such as convulsions, sensory impairment, and loss of consciousness. While antiepileptic treatments can control seizures for 70% of individuals with epilepsy, around 30% of seizures are not controllable. It stands to reason that an accurate seizure forecasting system would improve the health of individuals with drug-refractory epilepsy. Traditional machine learning seizure prediction methods rely on real-valued representations of EEG signals, failing to exploit the phase and magnitude information present within complex-valued transforms of EEG signals. In this project, I propose the use of a complex-valued convolutional neural network to extract more informative spatial-temporal features within complex-valued representations of EEG signals.