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.
