A Sleep State Detection and Intervention System

Abstract

We implemented an environment for daily physiological tracking using an FPGA DE10 board with integrated hand-made sensors made of an Micro:bit microprocessor. The system is capable of live streaming collected data, performing data processing and modeling, and exporting brain state to a mobile app for mental intervention. The FPGA microcomputer is capable of electroencephalogram (EEG) data preprocessing, feature extraction, and brain state classification using deep learning models. The data rendering software on the mobile phone simulates a professional medical interface through which the user is able to connect the EEG device to the phone, relay the EEG data to the FPGA board, and render the brain state curve on the phone. The mobile app runs a sleep inducing program which can help the user get into sleep more easily and deeply. The sleep-inducing technique takes advantage of Delta waves, which is proven to have the effect of deepening a person’s sleep, when it is converted to sound wave and applied to the subject. Our system monitors the user’s sleep state and adjusts the volume of Delta wave sounds, together with a user chosen background music, according to the user’s sleep depth.