Brain-computer interface (BCI) systems enable communication and control without movement. Although advanced signal processing methods are used in BCI research, the output of a BCI is still unreliable, and the information transfer rates are very low compared with conventional human interaction interfaces such as keyboard and mouse. Therefore, improvements in signal classification methods and the exploitation of the learning skills of the user are required to compensate the unreliability of the BCI system. This work analyzes the response time of the Bremen-BCI based on steady-state visual evoked potentials (SSVEP) previously tested on 27 subjects, and presents an enhanced method for faster detection of SSVEP responses. The aim is toward the development of a swift BCI (sBCI) that robustly detects the exact time point where the user starts modulating his brain signals.