Application of Kurtosis Based Dynamic Window to Enhance SSVEP Recognition

Steady-state visual evoked potential (SSVEP) is one of the main paradigms in the field of brain-computer interface (BCI). However, the challengeable issues for SSVEP are still how to make decisions from electroencephalogram to get a higher accuracy with a shorter time on recognition. In recent years, calibrated-free SSVEP algorithms have been constantly innovated and improved. As an effective approach, the dynamic window has been used to intercept EEG signals for recognition, and improving the information transfer rate (ITR) has become a hot research point. In this paper, the properties of the kurtosis feature were applied to select an appropriate kurtosis value as the threshold of SSVEP calibrated-free algorithm. To improve the accuracy of target recognition in the shortest possible time to achieve improvement of ITR, the length of the time window can be adjusted according to the threshold. For evaluation, the Benchmark dataset and four algorithms (Multivariate Synchro-nization Index (MSI), Canonical Correlation Analysis (CCA), Temporally Local Canonical Correlation Analysis (TCCA), and Filter Bank Canonical Correlation Analysis (FBCCA)) were applied to evaluate the recognition effect of dynamic window based on kurtosis. Experimental results showed that when the kurtosis is between 3.5 and 4, the performance of average ITR could achieve the best effect, and the highest ITR could reach up to 352.90 bits/min. In addition, this method was used in the 2021 BCI Robot Contest in World Robot Conference Contest. Using the strategy of CCA combining kurtosis value for dynamic window, the average ITR of five subjects was achieved 114.94 bits/min, and our team ranked fifth in the final contest.