Steady State Visual Evoked Potentials (SSVEPs) have been widely used in Brain-Computer Interfaces (BCIs). SSVEP-BCIs have advantages of high classification accuracy, high information transfer rate, and strong anti-interference ability. Traditional studies mostly used low/medium frequency SSVEPs as system control signals. However, visual flickers with low/medium frequencies are uncomfortable, and even cause visual fatigue and epilepsy seizure. High-frequency SSVEP is a promising approach to solve these problems, but its miniature amplitude and low signal-to-noise ratio (SNR) would pose great challenges for target recognition. This study developed an innovative BCI paradigm to enhance the SNR of high-frequency SSVEP, which is named Steady-State asymmetrically Visual Evoked Potential (SSaVEP). Ten characters were encoded by ten couples of asymmetric flickers whose durations only lasted one second and frequencies ranged from 31 to 40 Hz with a step of 1 Hz. Discriminative canonical pattern matching (DCPM) was used to decode the high-frequency SSaVEP signals. Four subjects participated in the offline experiment. As a result, the accuracy achieved an average of 87.5% with a peak of 97.1%. The simulated online information transfer rate reached 87.2 bits/min on average and 111.2 bits/min for maximum. The results of this study demonstrate the high-frequency SSaVEP paradigm is a promising approach to alleviate the discomfort caused by visual stimuli and thereby can broaden the applications of BCIs.