Improve the Classification Efficiency of High-Frequency Phase-Tagged SSVEP by a Recursive Bayesian-Based Approach.

Among the Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the phase-tagged SSVEP (p-SSVEP) has been proved a reliable paradigm to extend the number of available targets, especially for high-frequency SSVEP-based BCIs. However, the recognition efficiency of the high-frequency p-SSVEP still remains relatively low. A longer data segment may achieve a higher classification accuracy, but the time consumption of computation leads to the decrease of information transfer rate. This paper presents a recursive Bayesian-based approach to improve the high-frequency p-SSVEP classification efficiency. In each signal processing period, the classification result is generated by the current scores, the condition probability and a recursive prior probability (dynamic prior probability). The experiment displays the SSVEP stimuli with 20 Hz and 30 Hz respectively, and each frequency contains six phases. This paper compared three classification approaches and the recursive Bayesian-based approach could obtain the highest classification accuracy and practical bit rate under the same data length. The mean accuracy and practical bit rate were 89.7% and 37.8 bits/min with 20Hz, and 89.0% and 36.5 bits/min with 30Hz, respectively Furthermore, the recursive Bayesian-based approach could reduce the individual differences among different subjects. Therefore, the recursive Bayesian-based approach can lead to high classification efficiency in high-frequency p-SSVEP.