A robotic arm is a mechanical device with a given number of Degrees of Freedom (DoFs) that mimics the functions of a human arm and performs any desired task, such as grasping and moving objects. Current research is directed toward the design of robots and artificial human body parts controlled by brain signals, translating human thoughts into actions. Brain-Computer Interface (BCI) systems have been used to enable people with motor disabilities to control assistive robotic equipment that replaces the lost functions. This paper presents a review of the state-of-the-art of the latest papers dealing with the control of a robotic arm based on Electroencephalogram (EEG). A comparative study of the different methods and techniques used in different blocks of the robotic arm’s noninvasive BCI controlling system is conducted. These blocks include signal acquisition using noninvasive electrodes, signal preprocessing, feature extraction, classification, and command. Additionally, this paper presents a performance comparison of the reviewed controlling systems of robotic arms using EEG signals.