The Research And Design Of EEG Acquisition System Based On FPGA Under The TMS

Transcranial Magnetic Stimulation(TMS) is a painless, noninvasive green treatment. Magnetic field can directly effect on the brain without attenuation, which is not only be practical used to the stimulation of the brain, but also to the peripheral nerve muscle. Now, TMS has be widly used. By changing the stimulus frequency, magnetic stimulation signal could achieve to excite or inhibit the local cerebral cortex function. With the use of existing technology of TMS, it has significant effects on brain electrical signal. However, there needs a EEG acquisition equipment used under the TMS for furrher study of the impact of TMS for EEG acquisition.A biological electrical signal acquisition device used under TMS based on Field-Programmable Gate Array(FPGA) is designed in this dissertation. TMS will induce a strong electric field at the surface of skin, which can make the amplifer in saturated state for a long duration, meanwhile, the noise from power supply will also be introduced by TMS into the signal acquisition device. Aiming at the noise and amplifier saturation problem, the conventional biological electrical signal acquisition device was improved by introducing the concept of full-bandwidth amplifier, which could do attenuation of the DC component of signal acquisition device and ease the saturated state of amplifier effectively,instead of filtered out. In addition, TMS frequency should match up the brain electrical signal acquisition. Within the effective time after the TMS, the stimulus make the minimal impact to EEG acquisition.The complete design of an EEG acquistition system is explained in this dissertation, including the front amplifier, A/D conversion circuit, digital processing part, data transmission and the data receive and display on the host system. Then the implement of the main algorithms on the FPGA platform are explained, include main code for data acquistition, USB communication, digital filters. Issues on programming and algorithm optimization are also discussed.