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Zhao S, Wang R, Bao R, Yang L. Spatially-coded SSVEP BCI without pre-training based on FBCCA. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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Zhang R, Xu Z, Zhang L, Cao L, Hu Y, Lu B, Shi L, Yao D, Zhao X. The effect of stimulus number on the recognition accuracy and information transfer rate of SSVEP-BCI in augmented reality. J Neural Eng 2022; 19. [PMID: 35477130 DOI: 10.1088/1741-2552/ac6ae5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 04/26/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The biggest advantage of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) lies in its large command set and high information transfer rate (ITR). Almost all current SSVEP-BCIs use a computer screen (CS) to present flickering visual stimuli, which limits its flexible use in actual scenes. Augmented reality (AR) technology provides the ability to superimpose visual stimuli on the real world, and it considerably expands the application scenarios of SSVEP-BCI. However, whether the advantages of SSVEP-BCI can be maintained when moving the visual stimuli to AR glasses is not known. This study investigated the effects of the stimulus number for SSVEP-BCI in an AR context. APPROACH We designed SSVEP flickering stimulation interfaces with four different numbers of stimulus targets and put them in AR glasses and a CS to display. Three common recognition algorithms were used to analyze the influence of the stimulus number and stimulation time on the recognition accuracy and ITR of AR-SSVEP and CS-SSVEP. MAIN RESULTS The amplitude spectrum and signal-to-noise ratio of AR-SSVEP were not significantly different from CS-SSVEP at the fundamental frequency but were significantly lower than CS-SSVEP at the second harmonic. SSVEP recognition accuracy decreased as the stimulus number increased in AR-SSVEP but not in CS-SSVEP. When the stimulus number increased, the maximum ITR of CS-SSVEP also increased, but not for AR-SSVEP. When the stimulus number was 25, the maximum ITR (142.05 bits/min) was reached at 400 ms. The importance of stimulation time in SSVEP was confirmed. When the stimulation time became longer, the recognition accuracy of both AR-SSVEP and CS-SSVEP increased. The peak value was reached at 3 s. The ITR increased first and then slowly decreased after reaching the peak value. SIGNIFICANCE Our study indicates that the conclusions based on CS-SSVEP cannot be simply applied to AR-SSVEP, and it is not advisable to set too many stimulus targets in the AR display device.
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Affiliation(s)
- Rui Zhang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China, Zhengzhou university, Zhengzhou, 450000, CHINA
| | - Zongxin Xu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China , Zhengzhou university, Zhengzhou, Henan, 450001, CHINA
| | - Lipeng Zhang
- Zhengzhou University, Zhengzhou university, Zhengzhou, 450001, CHINA
| | - Lijun Cao
- Zhengzhou University, Zhengzhou university, Zhengzhou, 450000, CHINA
| | - Yuxia Hu
- Zhengzhou University, Zhengzhou university, Zhengzhou, 450001, CHINA
| | - Beihan Lu
- Zhengzhou University, Zhengzhou university, Zhengzhou, 450001, CHINA
| | - Li Shi
- Department of Automation, Tsinghua University, BeiJing, Beijing, P. R, 100084, CHINA
| | - Dezhong Yao
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan Province, chengdu, sichuan, 610054, CHINA
| | - Xincan Zhao
- Zhengzhou University, Zhengzhou university, Zhengzhou, 450001, CHINA
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Cherloo MN, Amiri HK, Daliri MR. Spatio-Spectral CCA (SS-CCA): A Novel Approach for Frequency Recognition in SSVEP-Based BCI. J Neurosci Methods 2022; 371:109499. [DOI: 10.1016/j.jneumeth.2022.109499] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 01/31/2022] [Accepted: 02/08/2022] [Indexed: 12/24/2022]
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Chang L, Wang R, Zhang Y. Decoding SSVEP patterns from EEG via multivariate variational mode decomposition-informed canonical correlation analysis. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103209] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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