The purpose of this thesis is to study the possibility of developing a computer interface that makes use of electroencephalogram (EEG) signals in order to improve the interaction between humans and computers. The major objective is to develop a system that is able to read brain activity in real time and then transform that information into instructions that can be executed on a computer. This study combines electroencephalogram (EEG) technology with sophisticated machine learning algorithms in order to develop an interface that is fluid, responsive, and user-friendly. Among the most important goals are the development of methods for robust signal processing, the design of an interface that is easy to use, and the installation of tools for real-time data presentation and analysis. A comprehensive set of tests was performed on the system, which revealed considerable improvements in terms of accuracy, responsiveness, and the overall user experience. This research makes a significant contribution to the expanding field of neurotechnology by providing an interface that is simple to use and may be used as an inspiration for a broad variety of purposes, including medical diagnosis and treatment, as well as entertainment and other games.