Authentication systems are crucial in today's digital world to safeguard private data and provide safe access to a range of platforms. Even though they are frequently used, traditional techniques like passwords, PINs, and biometrics might be difficult for people with impairments. For example, those with mobility disabilities might not be able to use fingerprints or facial identification, while people with cognitive impairments can find it challenging to remember complicated passwords. This highlights the urgent need for more inclusive authentication solutions. One promising approach is the use of electrooculography (EOG), which leverages eye movements for user authentication. EOG signals, which measure the electrical activity generated by eye movements, offer a noninvasive and easily observable means of neural activity. The mentioned system uses a single-channel setup to capture EOG signals, with Python libraries handling data processing. The graphical user interface (GUI) translates eye movements into specific actions, providing immediate feedback to the user. By identifying distinct EOG signal patterns corresponding to upward, downward, and blinking movements, the system offers a reliable and user-friendly authentication process. This technology is particularly beneficial for individuals with disabilities, enhancing both digital accessibility and security.