Amplitude Modulation Depth Coding Method for SSVEP-Based Brain–Computer Interfaces
In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the limited availability of frequency resources inherently constrains the scale of the instruction set, presenting a substantial challenge for efficient communication. As the number of stimuli increases, the comf...
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2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10839059/ |
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| author | Ruxue Li Zhenyu Wang Xi Zhao Guiying Xu Honglin Hu Ting Zhou Tianheng Xu |
| author_facet | Ruxue Li Zhenyu Wang Xi Zhao Guiying Xu Honglin Hu Ting Zhou Tianheng Xu |
| author_sort | Ruxue Li |
| collection | DOAJ |
| description | In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the limited availability of frequency resources inherently constrains the scale of the instruction set, presenting a substantial challenge for efficient communication. As the number of stimuli increases, the comfort level of the stimulus interface also becomes increasingly demanding due to the expanded flickering area. To address these issues, we proposed a novel amplitude modulation depth coding (AMDC) method that employs Amplitude Shift Keying (ASK) technique to modulate the luminance level of stimuli dynamically. Each stimulus with a single carrier frequency was assigned a specific binary sequence to operate two modulation depths. Two experiments were conducted to comprehensively assess the effectiveness of this approach. In Experiment 1, the time-frequency responses at two modulation depths across different frequencies were examined. A 36-target paradigm based on AMDC strategy was designed and evaluated in terms of user experience and classification performance in Experiment 2. The results show that the proposed paradigm obtains an average classification accuracy of <inline-formula> <tex-math notation="LaTeX">$81.7~\pm ~12.6$ </tex-math></inline-formula>% with an average information transfer rate (ITR) of <inline-formula> <tex-math notation="LaTeX">$45.4~\pm ~11.5$ </tex-math></inline-formula> bits/min. Moreover, it significantly reduces flicker perception and improves comfort level compared to traditional SSVEP stimuli with uniform modulation depth. Given its capability to improve coding efficiency for a single frequency and improve user experience, this method shows promising potential for application in large-scale command SSVEP-based BCI systems. |
| format | Article |
| id | doaj-art-b0ae507f158248feb299cd47f2e9235c |
| institution | Kabale University |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-b0ae507f158248feb299cd47f2e9235c2025-01-21T00:00:11ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-013339140310.1109/TNSRE.2025.352840910839059Amplitude Modulation Depth Coding Method for SSVEP-Based Brain–Computer InterfacesRuxue Li0https://orcid.org/0000-0003-3376-2020Zhenyu Wang1https://orcid.org/0000-0002-9363-9449Xi Zhao2https://orcid.org/0000-0001-8019-5423Guiying Xu3https://orcid.org/0000-0001-6497-614XHonglin Hu4https://orcid.org/0000-0002-4665-5278Ting Zhou5https://orcid.org/0000-0002-0420-0566Tianheng Xu6https://orcid.org/0000-0002-7152-1378Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, ChinaShanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, ChinaSchool of Microelectronics, Shanghai University, Shanghai, ChinaSchool of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, ChinaShanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, ChinaSchool of Microelectronics, Shanghai University, Shanghai, ChinaShanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, ChinaIn steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the limited availability of frequency resources inherently constrains the scale of the instruction set, presenting a substantial challenge for efficient communication. As the number of stimuli increases, the comfort level of the stimulus interface also becomes increasingly demanding due to the expanded flickering area. To address these issues, we proposed a novel amplitude modulation depth coding (AMDC) method that employs Amplitude Shift Keying (ASK) technique to modulate the luminance level of stimuli dynamically. Each stimulus with a single carrier frequency was assigned a specific binary sequence to operate two modulation depths. Two experiments were conducted to comprehensively assess the effectiveness of this approach. In Experiment 1, the time-frequency responses at two modulation depths across different frequencies were examined. A 36-target paradigm based on AMDC strategy was designed and evaluated in terms of user experience and classification performance in Experiment 2. The results show that the proposed paradigm obtains an average classification accuracy of <inline-formula> <tex-math notation="LaTeX">$81.7~\pm ~12.6$ </tex-math></inline-formula>% with an average information transfer rate (ITR) of <inline-formula> <tex-math notation="LaTeX">$45.4~\pm ~11.5$ </tex-math></inline-formula> bits/min. Moreover, it significantly reduces flicker perception and improves comfort level compared to traditional SSVEP stimuli with uniform modulation depth. Given its capability to improve coding efficiency for a single frequency and improve user experience, this method shows promising potential for application in large-scale command SSVEP-based BCI systems.https://ieeexplore.ieee.org/document/10839059/Brain-computer interfaceselectroencephalographysteady-state visual evoked potentialsamplitude modulation depth coding |
| spellingShingle | Ruxue Li Zhenyu Wang Xi Zhao Guiying Xu Honglin Hu Ting Zhou Tianheng Xu Amplitude Modulation Depth Coding Method for SSVEP-Based Brain–Computer Interfaces IEEE Transactions on Neural Systems and Rehabilitation Engineering Brain-computer interfaces electroencephalography steady-state visual evoked potentials amplitude modulation depth coding |
| title | Amplitude Modulation Depth Coding Method for SSVEP-Based Brain–Computer Interfaces |
| title_full | Amplitude Modulation Depth Coding Method for SSVEP-Based Brain–Computer Interfaces |
| title_fullStr | Amplitude Modulation Depth Coding Method for SSVEP-Based Brain–Computer Interfaces |
| title_full_unstemmed | Amplitude Modulation Depth Coding Method for SSVEP-Based Brain–Computer Interfaces |
| title_short | Amplitude Modulation Depth Coding Method for SSVEP-Based Brain–Computer Interfaces |
| title_sort | amplitude modulation depth coding method for ssvep based brain x2013 computer interfaces |
| topic | Brain-computer interfaces electroencephalography steady-state visual evoked potentials amplitude modulation depth coding |
| url | https://ieeexplore.ieee.org/document/10839059/ |
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