 This study proposes a novel frequency-specific, FS, algorithm framework for enhancing control state detection using short data length towards high-performance asynchronous steady-state visual evoked potential, SSVEP-based brain-computer interfaces, BCI. The FS framework sequentially incorporates task-related component analysis, TRCA-based SSVEP identification and a classifier bank containing multiple FS control state detection classifiers. For an input EEG EPIC, the FS framework first identifies its potential SSVEP frequency using the TRCA-based method and then recognizes its control state using one of the classifiers trained on the features specifically related to the identified frequency. A frequency unified, FU, framework that conducts control state detection using a unified classifier trained on features related to all candidate frequencies is proposed to compare with the FS framework. Offline evaluation using data lengths within one second found that the FS framework achieved excellent performance and significantly outperforms the FU framework. 14 target FS and FU asynchronous systems are separately constructed by incorporating a simple dynamic stopping strategy and validated using a Q-guided selection task in an online experiment using average data length of this article was authored by Yufeng Ku, Jala Du, Shuang Lu, and others. We are article.tv, links in the description below.