The topic of paralysis has gained a lot of interest among scientist over the last years. Therefore, many projects were made for patients suffering from paralysis, yet none has succeeded in achieving an effective way to give these patients the ability to control their paralyzed body parts. This paper proposes a robotic upper-limb exoskeleton guided using 3D object detection and recognition and controlled via Electroencephalogram (EEG) signals. The proposed system is dedicated to patients with complete monoplegic, hemiplegic and quadriplegic paralysis. The main objective of the system is to give these patients the ability to move their upper-limb, and control a wheelchair using their thoughts, which offers them independence, better life quality and assist them in leading active roles in the society. This system consists of four main components, namely, EEG module, infrared (IR) depth camera, 3D printed upper-limb exoskeleton and a motorized wheelchair. The former two are used as inputs to the system and the collaboration between them shows the uniqueness of the proposed approach. EEG signals are segmented and classified through Fuzzy Logic technique and the results are used for choosing the desired object for grabbing from the surface of a table. Movement to the desired object is executed based on the 3D coordinates obtained from the IR depth camera, while inertial measurement unit (IMU) sensor is placed on the arm as a feedback element to ensure accurate movement and proper safety measures. System prototype showed sufficient results for the proposed idea. © 2018 IEEE.