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Zhang S, Chen X. Effect of background luminance of visual stimulus on elicited steady-state visual evoked potentials. BRAIN SCIENCE ADVANCES 2022. [DOI: 10.26599/bsa.2022.9050006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Steady-state visual evoked potential (SSVEP)-based brain– computer interfaces (BCIs) have been widely studied. Considerable progress has been made in the aspects of stimulus coding, electroencephalogram processing, and recognition algorithms to enhance system performance. The properties of SSVEP have been demonstrated to be highly sensitive to stimulus luminance. However, thus far, there have been very few reports on the impact of background luminance on the system performance of SSVEP-based BCIs. This study investigated the impact of stimulus background luminance on SSVEPs. Specifically, this study compared two types of background luminance, i.e., (1) black luminance [red, green, blue (rgb): (0, 0, 0)] and (2) gray luminance [rgb: (128, 128, 128)], and determined their effect on the classification performance of SSVEPs at the stimulus frequencies of 9, 11, 13, and 15 Hz. The offline results from nine healthy subjects showed that compared with the gray background luminance, the black background luminance induced larger SSVEP amplitude and larger signal-to-noise ratio, resulting in a better classification accuracy. These results suggest that the background luminance of visual stimulus has a considerable effect on the SSVEP and therefore has a potential to improve the BCI performance.
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Affiliation(s)
- Shangen Zhang
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Xiaogang Chen
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China
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Zhang S, Han X, Chen X, Wang Y, Gao S, Gao X. A study on dynamic model of steady-state visual evoked potentials. J Neural Eng 2018; 15:046010. [DOI: 10.1088/1741-2552/aabb82] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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