A visual parallel-BCI speller based on the time-frequency coding strategy
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Updated: May 2, 2026
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
Published on: September 8, 2023
Minpeng Xu1, Long Chen, Lixin Zhang
1Department of Biomedical Engineering, Tianjin University, Tianjin, People's Republic of China. Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin, People's Republic of China.
Objective:
Spelling is one of the most important issues in brain-computer interface (BCI) research. This paper is to develop a visual parallel-BCI speller system based on the time-frequency coding strategy in which the sub-speller switching among four simultaneously presented sub-spellers and the character selection are identified in a parallel mode.
Approach:
The parallel-BCI speller was constituted by four independent P300+SSVEP-B (P300 plus SSVEP blocking) spellers with different flicker frequencies, thereby all characters had a specific time-frequency code. To verify its effectiveness, 11 subjects were involved in the offline and online spellings. A classification strategy was designed to recognize the target character through jointly using the canonical correlation analysis and stepwise linear discriminant analysis.
Main Results:
Online spellings showed that the proposed parallel-BCI speller had a high performance, reaching the highest information transfer rate of 67.4 bit min(-1), with an average of 54.0 bit min(-1) and 43.0 bit min(-1) in the three rounds and five rounds, respectively.
Significance:
The results indicated that the proposed parallel-BCI could be effectively controlled by users with attention shifting fluently among the sub-spellers, and highly improved the BCI spelling performance.
