Abstract
Information transfer rate (ITR) of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) spellers were often calculated with fixed gaze shifting time. However, the required gaze shifting time changes with the distance between the two characters. In this study, we propose a continuous spelling method to enhance the ITR of the speller by adaptively adjusting the gaze shifting time. In the continuous spelling procedure, SSVEP signals corresponding to a character sequence were evoked by focusing on different target stimuli continuously. To recognize these target characters, SSVEP segments and their onset time are first obtained by a threshold-based method using continuous wavelet transform (CWT) analysis. Then, we proposed a template reconstruction canonical correlation analysis (trCCA) to extract the feature of the SSVEP segments. Both offline and online experiments were conducted with 11 participants by a 12-target speller. The offline experiments were used to learn the parameters for template reconstruction. In online experiments, the proposed spelling method reached the highest ITRs of 196.41 ± 30.25 bits/min. These results demonstrate the feasibility and efficiency of the proposed method in SSVEP spelling systems.
This work is supported by the Foundation of National Key Laboratory of Human Factors Engineering (Grant NO. 6142222210101), the Key Industry Innovation Chain Projects of Shaanxi, China (Grant NO. 2021ZDLGY07-04).
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All authors would like to thank all the participants for data acquisition in this study.
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Xiong, B., Huang, J., Wan, B., Jiang, C., Su, K., Wang, F. (2023). A High-Speed SSVEP-Based Speller Using Continuous Spelling Method. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1791. Springer, Singapore. https://doi.org/10.1007/978-981-99-1639-9_18
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