A partial least squares-based stimulus frequency recognition model for steady-state visual evoked potentials detection

With shorter calibration times and higher information transfer rates, steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been studied most activity in recent years. Target identification is the ongoing core task in BCI researches, and plays a significant role in practical applications. In order to improve the performance of SSVEP-based BCI system, we proposed a partial least squares (PLS)-based stimulus frequency recognition model for SSVEP detection. Moreover, we compared the proposed method with canonical correlation analysis (CCA) and least absolute shrinkage and selection operator (LASSO) method, respectively. The experiment results showed that PLS can not only extract the SSVEP features effectively, but also can increase the classification accuracies of SSVEP-based BCI systems.