The Brain Computer Interface (BCI) is the communication between the human brain and the computer. Electroencephalogram (EEG) is one of the biomedical signals which can be obtained by attaching electrodes to the scalp. Some EEG related applications can be developed to help disabled people, such as EEG based wheelchair or robotic arm. A hybrid BCI real-time control system is proposed to control a multi-tasks BCI robot. In this system, a sliding window based online data segmentation strategy is proposed to segment training data, which enable the system to learn the dynamic features when the subject's brain state transfer from a rest state to a task execution state. The features help the system achieve real-time control and ensure the continuity of executing actions. In addition, Common Spatial Pattern (CSP) can better extract the spatial features of these continuous actions from the dynamic data to ensure that multiple control commands are accurately classified. In the experiment, three subjects' EEG data is collected, trained and tested the performance and reliability of the proposed control system. The system records the robot's spending time, moving distance, and the number of objects pushing down. Experimental results are given to show the feasibility of the real-time control system. Compared to real-time remote controller, the proposed system can achieve similar performance. Thus, the proposed hybrid BCI real-time control system is able to control the robot in the real-time environment and can be used to develop robot-aided arm training methods based on neurological rehabilitation principles for stroke and brain injury patients.