Sigma delta modulator in wearable brain wave recording system for personalized healthcare

Abstract

An electroencephalogram (EEG) is a critical brain wave signal which contains information that is of great use corresponding to various states of a brain. Signal analysis of EEG is an effective tool for early diagnosis of mental and neurological disorders, including autism, Attention-Deficit/Hyperactivity Disorder (ADHD), dementia, Alzheimer’s disease, etc. Commercially available EEG sensing devices of this time are usually very large, expensive, power-consuming, and is cumbersome if the user/patient needs to keep the device is close proximity, having the demand to monitor their health daily. A miniatured wearable EEG monitoring system is certainly much preferred for remote monitoring and diagnosis and diagnosis for personalized daily healthcare come true. The core of a miniaturized wearable EEG monitoring system are sensors and signal processors. A data converter is needed to build a bridge bonding the analogue output of sensors and digital input signal for the signal processor. This implies that an ADC is crucial to connecting the two cores of the wearable EEG system. Sigma-delta ADCs are a type of high-resolution, low-speed, and low-power ADC that operates by oversampling a signal, followed by noise-shaping, and a decimation filter. They are typically used where a high-resolution conversion of a low-frequency signal is required, such as audio, EEG signal and industrial control applications. Sigma-delta ADCs have several advantages, including low power consumption, low cost, and high dynamic range. Futhermore, they are less susceptible or sensitive to interference and noise because of their noiseshaping capabilities. This dissertation serves to examine the principles and operating characteristics of first-order and second-order sigma-delta ADCs, and discusses the advantages and drawbacks of this type of converter.