Implementation of neurobiological and various signal processing algorithms using FPGA circuits

Signal processing is an important part of computer science, which is used in but not limited to automation, pattern recognition, control theory, artificial intelligence, and networking and communication. A signal can be anything, which is measurable and everything that can be measured is also can and inevitably will be processed using analog or digital signal processing methods. It can be a temperature measurement, an image from a camera or a recording of neural brain activity, all of them have to be processed in some way. In signal processing the first step is to measure a physical quantity. After the measurement the quantity can be filtered and amplified. In ideal circumstances a noise-free and proper amplitude quantity can be converted to a digital signal using an analog-digital converter (ADC). This digital signal can be processed by a Central Processing Unit (CPU) / microprocessor, a microcontroller, a Graphics Processing Unit (GPU), a Field Programmable Gate Array (FPGA) or an Application-Specific Integrated Circuit (ASIC). Changing the hardware in signal processing applications are a complex task and usually requires to modify an optimised software. In many cases only software optimisation offers limited improvements, although the time and energy investment is still significant. In these cases the usage of FPGA makes possible to rapidly change the hardware architecture along with the software and also offers real-time and efficient operation. The PhD thesis presents FPGA-based real-time signal processing, including the detection and classification of action potential in neurophysiological measurements. A common feature of the presented approaches is real-time implementation using FPGA. The dissertation consists of three major parts. The first chapter presents real-time detection and synthesis of neurophysiological signals based on FPGA, the second chapter presents real-time classification of neurophysiological signals based on spatial information, while the third chapter presents the application of FPGA-based systems in signal processing and simulation