Analysis of Control System and Signal Gain on Internet of Things-Based Electroencephalograph Circuit

Abstract

Epilepsy, characterized by recurrent seizures, exhibits changes in brain signals with amplitudes far exceeding normal conditions (up to 800µV versus 5-200µV). The varying phases of seizures (from a few seconds to minutes and accompanied by tonic-clonic seizures) pose a risk, especially in public spaces. In this case, we designed a wearable, portable EEG that provides real-time seizure detection. Our design uses four voltage amplifiers with good accuracy and one filter circuit (differential: 97.83%, non-inverting: 96.92%, 96.5%, and 97.99%). The signal is amplified for detection, then filtered for optimal compatibility with the microcontroller. In addition to amplified brain signals, balance, light, and GPS sensors are used as binary indicators of epileptic conditions. This data is transmitted via the IoT system, showing Quality of Service in the Good and Very Good categories in the parameters Packet Loss, Throughput, Delay and Jitter. It can be concluded that the transmission data system shows data in the good category and jitter value in the very good category.