LAUSR

Steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) systems can be realised using only one electrode; however, due to the inter-user and inter-trial differences, the handling of multiple electrode is preferred. This raises the problem of evaluating information from multiple electrode signals. To solve this problem, we developed a novel spatial filtering method (Generated Reference Filter) for SSVEP-based BCIs. In our method an artificial reference signal is generated by a combination of reference electrode signals. Multiple regression analysis (MRA) was used to determine the optimal weight coefficients for signal combination. The filtered signal was obtained by subtraction. The method was tested on a SSVEP dataset and compared with minimum energy combination and common reference methods, namely the surface Laplacian technique and common average referencing. The newly developed method provided more effective filtering and therefore higher SSVEP detection accuracy was obtained. It was also more robust against subject-to-subject and trial-to-trial variability as the artificial reference signal was recalculated for each detection round. No special preparation is required, and the method is easy to implement. These experimental results indicate that the proposed method can be used confidently with SSVEP-based BCI systems.