Reduced calibration by efficient transformation of templates for high speed hybrid coded SSVEP brain-computer interfaces

Brain-computer interfacing (BCI) based on steady-state visual evoked potentials (SSVEPs) is one of the most promising techniques due to its high performance. A state-of-the-art is a BCI based on hybrid frequency and phase coded SSVEP, which needs a large set of calibration data as reference signals, so-called individual templates. The aim of this study is to propose an approach to calibration reduction by generating from individual templates corresponding to a part of commands (source templates) to new templates corresponding to the rest of commands. The new templates can be obtained by shifting the frequency and phase of the source template to the desired frequency and phase. In this way, time and cost for calibration can be greatly reduced. The experimental results suggested that the proposed approach successfully transferred the source template, closely achieving the performance using the full calibration dataset.