This paper presents an empirical mode decomposition (EMD) and refined generalized zero crossing (rGZC) approach to achieve frequency recognition in steady-stated visual evoked potential (SSVEP)-based brain computer interfaces (BCIs). Six light emitting diode (LED) flickers with high flickering rates (30, 31, 32, 33, 34, and 35. Hz) functioned as visual stimulators to induce the subjects' SSVEPs. EEG signals recorded in the Oz channel were segmented into data epochs (0.75. s). Each epoch was then decomposed into a series of oscillation components, representing fine-to-coarse information of the signal, called intrinsic mode functions (IMFs). The instantaneous frequencies in each IMF were calculated by refined generalized zero-crossing (rGZC). IMFs with mean instantaneous frequencies (f̄GZC) within 29.5. Hz and 35.5. Hz (i.e., 29.5≤f̄GZC≤35.5 Hz) were designated as SSVEP-related IMFs. Due to the time-locked and phase-locked characteristics of SSVEP, the induced SSVEPs had the same frequency as the gazing visual stimulator. The LED flicker that contributed the majority of the frequency content in SSVEP-related IMFs was chosen as the gaze target. This study tests the proposed system in five male subjects (mean age = 25.4 ± 2.07 y/o). Each subject attempted to activate four virtual commands by inputting a sequence of cursor commands on an LCD screen. The average information transfer rate (ITR) and accuracy were 36.99 bits/min and 84.63%. This study demonstrates that EMD is capable of extracting SSVEP data in SSVEP-based BCI system.