IFT co-authored a paper with Prof. Wei Li from California State University, entitled  “Improve the Classification Efficiency of High-Frequency Phase-Tagged SSVEP by a Recursive Bayesian-based Approach” in IEEE Transactions on Neural Systems and Rehabilitation Engineering. 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. 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.