<p>Point of care (POC) systems refer to the systems where testing is performed right where the patient is. These types of systems are getting popular day by day. POC systems dedicated to detecting eye diseases and various eye conditions can be developed by hardware implementations of Electrooculogram (EOG). EOG is a bioelectric signal produced around the eyes. This signal provides information on eye movements which is beneficial in many medical and bio-electrical applications, such as diagnosing different ocular diseases and controlling human-computer interactions. Elevating EOG implementations at the proper hardware level is important as it can be directly utilized in diagnosis. In this paper, we systematically investigate the research trend on hardware implementations of EOG and review its state of the art in the last five years. About 24% of the studies used Field-Programmable Gate Arrays, while the remaining 76% were implemented with discrete circuits and microcontrollers. We discuss various EOG based processing techniques, their advantages, and limitations. We also review the performance of the EOG hardware based systems. Furthermore, we analyze and benchmark the performance of the various systems. EOG-based classification methods range from simple thresholding to advanced machine learning (ML) and deep learning (DL) approaches. Threshold- and signal-processing-based methods achieve 83–100% accuracy, while ML and DL models typically exceed 90–95%, offering higher scalability and robustness. In addition, we deduce future research directions of EOG and associated challenges. We hope this paper will provide a guideline for research in EOG POC systems.</p>