Abstract
In recent years, with the development of CMOS technology and the improvement of process, a number of integrated circuits have been used in biomedical applications. For instance, implantable brain-machine interfaces for treating Parkinson’s disease, epilepsy, and other diseases have attracted more and more attentions and research resources. This thesis aims to design the β wave detecting system during the stimulation for studying the mechanism of deep brain stimulation (DBS) and the therapy for the Parkinson’s disease. The continuous, periodic DBS not only inhibits abnormal neuron activities but also suppresses some normal physiological activities. Therefore, the closed-loop system is proposed. On the other hand, to analyze the brain wave from the STN and Motor Cortex is medical evidence related to this disease. In this research, the spectrum and different energy algorithm were used to detect the energy difference between the stimulation and without stimulation. Firstly we implemented the system through the frontend of the low noise amplifier and the Gm-C based band-pass filter to capture Beta band signal. Besides, the research also used the square and integrator circuit to calculate the average energy of the Beta band. In addition, to achieve low power consumption, all transistors in this system were designed in the subthreshold region. The system chip was fabricated and verified by using TSMC 018um 1P6M CMOS process.The total power of the system without buffer is less than10uw (Gm-C band-pass filter 1.8uW multiplier 0.428uW integrator 0.45uW)and input linear range can be reached 400mV.The chip total area is 200um*950um. The focus of the research includes discussion of the algorithm, functional verification of the module circuit, design implementation of integrated circuits, and chip verification for integration prototype system performance. This study will be helpful for designing a closed-loop and adaptive stimulation device in the implanted brain machine interface.