Sinusoid-assisted MEMD-based CCA method for SSVEP-based BCI improvement

Although the canonical correlation analysis (CCA) algorithm has been applied successfully to steady-state visual evoked potential (SSVEP) detection, artifacts and unrelated brain activities may affect the performance of SSVEP-based brain– computer interface systems. Extracting the characteristic frequency sub-bands is an effective method of enhancing the signal-to-noise-ratio of SSVEP signals. The sinusoid-assisted multivariate extension of empirical mode decomposition (SA-MEMD) algorithm is a powerful method of spectral decomposition. In this study, we propose an SA-MEMD-based CCA method for SSVEP detection. Experimental results suggest that the SA-MEMD-based CCA algorithm is a useful method for the detection of typical SSVEP signals. The SA-MEMD-based CCA algorithm reached a classification accuracy of 88.3% for a window of 4 s and outperformed the standard CCA algorithm by 2.8%.

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