IEEE Access (Jan 2022)

An SSPVEP Brain-Computer Interface Using a Small Amount of Flicker Stimuli

  • Xi Zhao,
  • Zhenyu Wang,
  • Ruxue Li,
  • Guiying Xu,
  • Ting Zhou,
  • Tianheng Xu,
  • Honglin Hu

DOI
https://doi.org/10.1109/ACCESS.2022.3188855
Journal volume & issue
Vol. 10
pp. 73257 – 73268

Abstract

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Brain-computer interface based on steady-state visual evoked potential (SSVEP-BCI) has been widely concerned because of its highest SNR of EEG signals. This kind of BCIs needs to modulate the control instructions with visual stimuli. However, a large number of visual stimuli will induce strong visual fatigue, and occupy a large area of screen space. As a result of the above disadvantages, the further application of this kind of BCIs can not be accepted by most users. It has been verified that 40 control instructions can be encoded with 20 flicker stimuli. The visual fatigue caused by SSVEP-BCIs can be alleviated by reducing the number of flicker stimuli. However, whether it is possible to encode the same number of control instructions with fewer flicker stimuli or not still remains unknown. This study will show the performance of the SSPVEP-BCIs after further removing some redundant flicker stimuli. We divide the proposed BCI system into four subsystems, and the performance of each subsystem will be shown in this paper. 12 flickering disks were used as visual stimuli to stimulate the brain to produce SSVEP responses. 12 targets were set on the flickering disks, and 28 targets were set alternately between the flicker stimuli, ensuring that always exist 1 to 2 flicker stimuli near each target. Manhattan distance based task-related component analysis (MTRCA) was used to process and decode the EEG signals in this paper. The performance within each of the subsystems was investigated and compared. When the sampling time was set to 4.6 s, the average accuracy of all subjects reaches 77.12%. This result shows that the 40-target BCI system based on proposed stimulation paradigm is available to the vast majority of users. Meanwhile, the accuracy of three subsystems is greater than 80% when the sampling time is 4.6 s. This result shows that the performance of SSPVEP stimulation paradigm is accepted in the application scenarios which only require a small number of instructions. In this paper, the research of SSPVEP paradigm is improved. It is revealed that using less flicker stimuli can still stimulate the brain to produce classifiable SSVEP responses. The conclusion of this study can guide the following study and application of SSPVEP-BCIs.

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