The goal of this ECE Senior Design Capstone project is to implement a system that reads brain signals from an electroencephalogram (EEG) and outputs a visualization of the user’s hand movement that was recorded. The final visualization is a prediction of the user’s movement that is derived from a series of signal processing techniques and feature selection methods. A machine learning model was trained in real time to make these movement predictions, and the output will be able to be directly compared to the motion recorded by a glove circuit to determine accuracy. The team implemented and researched various processing methods, including Common Average Referencing and Linear Discriminant Analysis, Common Spatial Patterns, and Principal Component Analysis and Independent Component Analysis, to compare performance and make recommendations for future research. Our biggest accomplishment is having an accuracy result of 74.3% for classifying movement using a support vector machine. It was difficult trying to meet the threshold of 60% accuracy for our engineering requirement. An area of improvement would be utilizing better data sets to hopefully classify more accurate hand movement.