Comparisons of stimulus paradigms for SSVEP-based brain–computer interfaces

<i>Objective.</i>With the recent development of visual evoked potential (VEP) based brain-computer interfaces (BCIs), the stimulus paradigm has been continuously innovated, in which the pursuit of higher BCI performance and better user experience has become indispensable.<i>Approach.</i>To optimize the stimulus paradigm, a 12-target online BCI system was designed in this study by adopting flicker for steady-state VEPs, Newton's ring for steady-state motion VEP, and frame rate based video stimulus, respectively. The signal characteristics of VEP, classification accuracy, and user experience of the three stimulus paradigms were quantitatively evaluated and compared.<i>Main results.</i>The online information transfer rates for the three stimulus paradigms were 53.77 bits min<sup>-1</sup>, 51.41 ± 3.55 bits min<sup>-1</sup>, and 52.07 ± 3.09 bits min<sup>-1</sup>, respectively. The video stimulus had a significantly better user experience, while the flicker stimulus showed the worst.<i>Significance.</i>These results demonstrate the advantage of the proposed video stimulus paradigm and have significant theoretical and applied implications for developing VEP-based BCI systems.

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Publication Details

Journal
Journal of Neural Engineering
Published
2025-04-01
DOI
https://doi.org/10.1088/1741-2552/adce32
Citations
2
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
2.86

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article

Comparisons of stimulus paradigms for SSVEP-based brain–computer interfaces

Xiaorong Gao, Weihua Pei, Guoya Dong, Yijun Wang et al.
2 citations
Journal of Neural Engineering
EEG and Brain-Computer Interfaces
2.86
article

Comparisons of stimulus paradigms for SSVEP-based brain–computer interfaces

Xiaorong Gao, Weihua Pei, Guoya Dong, Yijun Wang, Deyu Zhao
article en
2 citations

Abstract

<i>Objective.</i>With the recent development of visual evoked potential (VEP) based brain-computer interfaces (BCIs), the stimulus paradigm has been continuously innovated, in which the pursuit of higher BCI performance and better user experience has become indispensable.<i>Approach.</i>To optimize the stimulus paradigm, a 12-target online BCI system was designed in this study by adopting flicker for steady-state VEPs, Newton's ring for steady-state motion VEP, and frame rate based video stimulus, respectively. The signal characteristics of VEP, classification accuracy, and user experience of the three stimulus paradigms were quantitatively evaluated and compared.<i>Main results.</i>The online information transfer rates for the three stimulus paradigms were 53.77 bits min<sup>-1</sup>, 51.41 ± 3.55 bits min<sup>-1</sup>, and 52.07 ± 3.09 bits min<sup>-1</sup>, respectively. The video stimulus had a significantly better user experience, while the flicker stimulus showed the worst.<i>Significance.</i>These results demonstrate the advantage of the proposed video stimulus paradigm and have significant theoretical and applied implications for developing VEP-based BCI systems.

Journal of Neural EngineeringVol. 22(2)
Hebei University of Technology (CN), Chinese Academy of Sciences (CN), State Key Laboratory on Integrated Optoelectronics (CN), Chinese Academy of Surveying and Mapping (CN), Chinese Institute for Brain Research (CN), Institute of Semiconductors (CN), University of Chinese Academy of Sciences (CN), Tsinghua University (CN)
National Natural Science Foundation of China
Industry, innovation and infrastructure
Openalex Percentile: Top 11%
EEG and Brain-Computer Interfaces
2.86
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