Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs) have been investigated increasingly in the last years. This type of brain signals resulting from repetitive flicker stimulation has the same fundamental frequency as the stimulation including higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by localizing individual electroencephalogram (EEG) recording positions. In the current work, a 4-class SSVEP-based BCI system was set up. Ten subjects participated and EEG was recorded from 21 channels overlying occipital areas. Features were extracted by applying Discrete Fourier transformation and a lock-in analyzer system. A simple one versus the rest classifier was applied to compare methods and localize individual electrode positions. It was shown that the use of three SSVEP-harmonics recorded from individual channels yielded significantly higher classification accuracy compared to one harmonic and to the standard positions O1 and O2. Furthermore, the application of a simple one versus the rest classifier and the use of a lock-in analyzer system lead to a higher classification accuracy (mean+/-S.D., about 74+/-16%) in a 4-class BCI compared to the commonly used Discrete Fourier transformation (DFT, 62+/-14%). By applying a screening procedure, the optimal electrode positions for bipolar derivations can be detected. Furthermore, information about subject's specific 'resonance-like' frequency regions can be obtained by observing higher harmonics of the SSVEPs.