EEG Alarm Optimizer App

Description

Created an application that uses Electroencephalography (EEG) data to optimize alarm timing based on sleep cycles. The app uses a convolutional neural network (CNN) trained on EEG data to identify what sleep stage you are in and wakes you up during a lighter sleep stage in the time window requested, resulting in more refreshed awakening. The project involved developing a CNN for sleep stage classification and a front end interface to configure the alarm and view sleep data. Partners: Janet Shin, Tvisha Nepani, Sanjana Tawre, Amanda Zhang