Decision-Making Selector (DMS) for Integrating CCA-Based Methods to Improve Performance of SSVEP-Based BCIs

Objective: Recent research has demonstrated improved performance of a brain-computer interface (BCI) using fusion based approaches. This paper proposes a novel decision-making selector (DMS) to integrate classification decisions of different frequency recognition methods based on canonical correlation analysis (CCA) which were used in decoding steady state visual evoked potentials (SSVEPs). Methods: The DMS method selects a decision more likely to be correct from two methods namely as M1 and M2 by separating the M1-false and M2-false trials. To measure the uncertainty of each decision, feature vectors were extracted using the largest and second largest correlation coefficients corresponding to all the stimulus frequencies. The proposed method was evaluated by integrating all pairs of 7 CCA-based algorithms, including CCA, individual template-based CCA (ITCCA), multi-set CCA (MsetCCA), L1-regularized multi-way CCA (L1-MCCA), filter bank CCA (FBCCA), extended CCA (ECCA), and task-related component analysis (TRCA). Main results: The experimental results obtained from a 40-target dataset of thirty-five subjects showed that the proposed DMS method was validated to obtain an enhanced performance by integrating the algorithms with close accuracies. Conclusion: The results suggest that the proposed DMS method is effective in integrating decisions of different methods to improve the performance of SSVEP-based BCIs.