Evaluating the Feasibility of a Novel Approach for SSVEP Detection Accuracy Improvement Using Phase Shifts

Abstract

The canonical correlation analysis (CCA), double-partial least-squares (DPLS) methods and least absolute shrinkage and selection operator (LASSO) have been proven effectively in detecting the steady-state visual evoked potential (SSVEP) in SSVEP-based brain-computer interface systems. However, the accuracy of SSVEP classification can be affected by phase shifts of the electroencephalography data, so we explored the possibility of improving SSEVP detection using these methods at different phase shifts. After calculating the accuracy at different phases, we found that the phase shifts could affect the accuracy of SSVEP classification, the classification accuracy could improved about 1.1% mostly using the CCA method, meanwhile the comparison of the three methods was made at the same time and some differences between the CCA, DPLS and LASSO methods at the different phase shifts also be found. The results indicated that on the one hand, the accuracy of SSVEP detection was improved with the change of the phase, but on the other hand, although the three methods could obtain high classification accuracy, the DPLS and LASSO method showed larger fluctuations than the CCA method as the phase of the electroencephalography data of each participant or their average changed.