Hardware-Software Codesign of Electrooculogram Signal Processors

Abstract

Electrooculogram (EOG) is a bioelectric signal carrying eye movement information. This signal can be utilized in medical and bio-electrical applications such as diagnosing different ocular diseases and controlling human-computer interfaces. Point of care (POC) systems refer to the systems where testing is performed right where the patient is. POC systems dedicated to detecting various eye conditions can be developed by hardware implementations of EOG. Field Programmable Gate Arrays (FPGAs) are reconfigurable integrated circuits offering flexible software features with fast parallel computation. Application specific designs in FPGA fix the functionality and reduce the number of components used providing cost-effectiveness, and power efficiency. In this research, a systematic investigation of the research trend on hardware implementations of EOG is presented first. After that, an in-depth analysis of two novel FPGA-based architectures is presented. The first work aims to design a hardware-optimized binary EOG processor for blink detection by using multichannel EOG signals containing horizontal and vertical EOG signals. After preprocessing the EOG signals, by extracting only two features- root mean square (RMS) and standard deviation (STD), blink and saccades are classified employing support vector machine (SVM) with 97.5% accuracy. The implemented system of this design in Xilinx Zynq-7000 FPGA achieves an accuracy of 95%. The second work aims to design a hardware-optimized machine learning based EOG signal processor to classify six different eye movements (up, down, normal, right, left, and blink) adopting an SVM classifier with average software accuracy of 97.92%. The accuracy of the implemented system in Zynq UltraScale+ is 95.56%. The efficacy of the developed systems is proved by comparing them with state-of-the-art technologies. A few potential future research scopes are mentioned at the end of the thesis.