SSVEP-Based Brain-Computer Interface With a Limited Number of Frequencies Based on Dual-Frequency Biased Coding.

How to encode as many targets as possible with a limited-frequency resource is a difficult problem in the practical use of a steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) speller. To solve this problem, this study developed a novel method called dual-frequency biased coding (DFBC) to tag targets in a SSVEP-based 48-character virtual speller, in which each target is encoded with a permutation sequence consisting of two permuted flickering periods that flash at different frequencies. The proposed paradigm was validated by 11 participants in an offline experiment and 7 participants in an online experiment. Three occipital channels (O1, Oz, and O2) were used to obtain the SSVEP signals for identifying the targets. Based on the coding characteristics of the DFBC method, the proposed approach has the ability of self-correction and thus achieves an accuracy of 76.6% and 79.3% for offline and online experiments, respectively, which outperforms the traditional multiple frequencies sequential coding (MFSC) method. This study demonstrates that DFBC is an efficient method for coding a high number of SSVEP targets with a small number of available frequencies.