Among different brain–computer interfaces (BCIs), the steady-state visual evoked potential (SSVEP)-based BCI has been widely used because of its higher signal to noise ratio (SNR) and greater information transfer rate (ITR). In this paper, a method based on multiple signal classification (MUSIC) was proposed for multidimensional SSVEP signal processing. Both fundamental and second harmonics of SSVEPs were employed for the final target recognition. The experimental results proved it has the advantage of reducing recognition time. Also, the relation between the duty-cycle of the stimulus signals and the amplitude of the second harmonics of SSVEPs was discussed via experiments. In order to verify the feasibility of proposed methods, a two-layer spelling system was designed. Different subjects including those who have never used BCIs before used the system fluently in an unshielded environment.