Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface
An essential priority of visual brain-computer interfaces (BCIs) is to enhance the information transfer rate (ITR) to achieve high-speed communication. Despite notable progress, noninvasive visual BCIs have encountered a plateau in ITRs, leaving it uncertain whether higher ITRs are achievable. In th...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811924000430 |
| _version_ | 1797292980578549760 |
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| author | Nanlin Shi Yining Miao Changxing Huang Xiang Li Yonghao Song Xiaogang Chen Yijun Wang Xiaorong Gao |
| author_facet | Nanlin Shi Yining Miao Changxing Huang Xiang Li Yonghao Song Xiaogang Chen Yijun Wang Xiaorong Gao |
| author_sort | Nanlin Shi |
| collection | DOAJ |
| description | An essential priority of visual brain-computer interfaces (BCIs) is to enhance the information transfer rate (ITR) to achieve high-speed communication. Despite notable progress, noninvasive visual BCIs have encountered a plateau in ITRs, leaving it uncertain whether higher ITRs are achievable. In this study, we used information theory to study the characteristics and capacity of the visual-evoked channel, which leads us to investigate whether and how we can decode higher information rates in a visual BCI system. Using information theory, we estimate the upper and lower bounds of the information rate with the white noise (WN) stimulus. Consequently, we found out that the information rate is determined by the signal-to-noise ratio (SNR) in the frequency domain, which reflects the spectrum resources of the channel. Based on this discovery, we propose a broadband WN BCI by implementing stimuli on a broader frequency band than the steady-state visual evoked potentials (SSVEPs)-based BCI. Through validation, the broadband BCI outperforms the SSVEP BCI by an impressive 7 bps, setting a record of 50 bps. The integration of information theory and the decoding analysis presented in this study offers valuable insights applicable to general sensory-evoked BCIs, providing a potential direction of next-generation human-machine interaction systems. |
| first_indexed | 2024-03-07T20:05:17Z |
| format | Article |
| id | doaj.art-9efebd19a18742f5a1c7bb09cab4bbae |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-03-07T20:05:17Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-9efebd19a18742f5a1c7bb09cab4bbae2024-02-28T05:12:52ZengElsevierNeuroImage1095-95722024-04-01289120548Estimating and approaching the maximum information rate of noninvasive visual brain-computer interfaceNanlin Shi0Yining Miao1Changxing Huang2Xiang Li3Yonghao Song4Xiaogang Chen5Yijun Wang6Xiaorong Gao7Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, ChinaDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, ChinaDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, ChinaDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, ChinaDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, ChinaInstitute of Biomedical Engineering, Chinese Academy of Medical, Sciences and Peking Union Medical College, Street, Tianjin 300192, ChinaKey Laboratory of Solid-State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, ChinaDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China; Corresponding author.An essential priority of visual brain-computer interfaces (BCIs) is to enhance the information transfer rate (ITR) to achieve high-speed communication. Despite notable progress, noninvasive visual BCIs have encountered a plateau in ITRs, leaving it uncertain whether higher ITRs are achievable. In this study, we used information theory to study the characteristics and capacity of the visual-evoked channel, which leads us to investigate whether and how we can decode higher information rates in a visual BCI system. Using information theory, we estimate the upper and lower bounds of the information rate with the white noise (WN) stimulus. Consequently, we found out that the information rate is determined by the signal-to-noise ratio (SNR) in the frequency domain, which reflects the spectrum resources of the channel. Based on this discovery, we propose a broadband WN BCI by implementing stimuli on a broader frequency band than the steady-state visual evoked potentials (SSVEPs)-based BCI. Through validation, the broadband BCI outperforms the SSVEP BCI by an impressive 7 bps, setting a record of 50 bps. The integration of information theory and the decoding analysis presented in this study offers valuable insights applicable to general sensory-evoked BCIs, providing a potential direction of next-generation human-machine interaction systems.http://www.sciencedirect.com/science/article/pii/S1053811924000430Information rateTemporal response functionVisual BCIWhite noiseSSVEP |
| spellingShingle | Nanlin Shi Yining Miao Changxing Huang Xiang Li Yonghao Song Xiaogang Chen Yijun Wang Xiaorong Gao Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface NeuroImage Information rate Temporal response function Visual BCI White noise SSVEP |
| title | Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface |
| title_full | Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface |
| title_fullStr | Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface |
| title_full_unstemmed | Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface |
| title_short | Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface |
| title_sort | estimating and approaching the maximum information rate of noninvasive visual brain computer interface |
| topic | Information rate Temporal response function Visual BCI White noise SSVEP |
| url | http://www.sciencedirect.com/science/article/pii/S1053811924000430 |
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