In this paper, a novel electroencephalographic (EEG) based mind controlled virtual-human obstacle-avoidance platform (EEG-MC-VHOAP) is designed to improve brain computer interface (BCI) systems and offer a new game. With the EEG-MC-VHOAP, subjects can use their brain signals to control a virtual human to have a training of avoiding obstacles in a three dimensional (3D) environment. The EEG-MC-VHOAP is composed of an EEG based BCI subsystem and a 3D virtual-human subsystem. In the EEG-based BCI subsystem, a self-adaptive bayesian linear discriminant analysis(SA-BLDA) is adopted to classify the P300 signals, and is then transformed into four control commands. The control commands are used to control the virtual-human to walk forward, walk in a crouch, turn left and turn right. Three subjects were asked to attend the testing with the EEG-MC-VHOAP. All subjects accessed to a 100% train accuracy for repeating flashing 4-6 trails, a less than 10% average collision rate, and a higher than 80% online accuracy. Both the training and online testing results demonstrate the effectiveness of the proposed EEG-MC-VHOAP.