Leveraging Spatiotemporal Estimation for Online Adaptive Steady-State Visual Evoked Potential Recognition

Online adaptive canonical correction analysis (OACCA) has been applied successfully in the recently popular steady-state visual evoked potential (SSVEP) target recognition methods. However, due to the significant amount of spatiotemporal relevant background noise in the online historical sample label data of OACCA, there is redundant noise component in the learned common spatial filter that can reduce online classification accuracy. Aiming at solving this defect in OACCA, we designed an online spatial-temporal equalization filter (STE) to suppress the background noise component in the electroencephalography (EEG). Meanwhile, an adaptive decoding method for SSVEP based on online spatial-temporal estimation (STE-OACCA) is proposed by combining the online STE filter and the OACCA algorithm. A pseudoonline test on the Tsinghua University FBCCA-DW dataset shows that the proposed STE-OACCA method significantly outperforms the CCA, MSI, OACCA approaches as well as STE-CCA. More importantly, proposed method can be directly used in online SSVEP recognition without calibration. The proposed algorithm is robust, which is promising for the development of practical brain computer interface (BCI).