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  1. Article ; Online: Visual tracking brain-computer interface.

    Huang, Changxing / Shi, Nanlin / Miao, Yining / Chen, Xiaogang / Wang, Yijun / Gao, Xiaorong

    iScience

    2024  Volume 27, Issue 4, Page(s) 109376

    Abstract: Brain-computer interfaces (BCIs) offer a way to interact with computers without relying on physical movements. Non-invasive electroencephalography-based visual BCIs, known for efficient speed and calibration ease, face limitations in continuous tasks due ...

    Abstract Brain-computer interfaces (BCIs) offer a way to interact with computers without relying on physical movements. Non-invasive electroencephalography-based visual BCIs, known for efficient speed and calibration ease, face limitations in continuous tasks due to discrete stimulus design and decoding methods. To achieve continuous control, we implemented a novel spatial encoding stimulus paradigm and devised a corresponding projection method to enable continuous modulation of decoded velocity. Subsequently, we conducted experiments involving 17 participants and achieved Fitt's information transfer rate (ITR) of 0.55 bps for the fixed tracking task and 0.37 bps for the random tracking task. The proposed BCI with a high Fitt's ITR was then integrated into two applications, including painting and gaming. In conclusion, this study proposed a visual BCI based-control method to go beyond discrete commands, allowing natural continuous control based on neural activity.
    Language English
    Publishing date 2024-03-02
    Publishing country United States
    Document type Journal Article
    ISSN 2589-0042
    ISSN (online) 2589-0042
    DOI 10.1016/j.isci.2024.109376
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface.

    Shi, Nanlin / Miao, Yining / Huang, Changxing / Li, Xiang / Song, Yonghao / Chen, Xiaogang / Wang, Yijun / Gao, Xiaorong

    NeuroImage

    2024  Volume 289, Page(s) 120548

    Abstract: 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 ... ...

    Abstract 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.
    MeSH term(s) Humans ; Brain-Computer Interfaces ; Evoked Potentials, Visual ; Electroencephalography ; Signal-To-Noise Ratio ; Communication ; Photic Stimulation ; Algorithms
    Language English
    Publishing date 2024-02-19
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1147767-2
    ISSN 1095-9572 ; 1053-8119
    ISSN (online) 1095-9572
    ISSN 1053-8119
    DOI 10.1016/j.neuroimage.2024.120548
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Order via subito

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  3. Book ; Online: Estimating and approaching maximum information rate of noninvasive visual brain-computer interface

    Shi, Nanlin / Miao, Yining / Huang, Changxing / Li, Xiang / Song, Yonghao / Chen, Xiaogang / Wang, Yijun / Gao, Xiaorong

    2023  

    Abstract: The mission of visual brain-computer interfaces (BCIs) is to enhance information transfer rate (ITR) to reach high speed towards real-life communication. Despite notable progress, noninvasive visual BCIs have encountered a plateau in ITRs, leaving it ... ...

    Abstract The mission of visual brain-computer interfaces (BCIs) is to enhance information transfer rate (ITR) to reach high speed towards real-life 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 investigate the information rate limits of the primary visual channel to explore whether we can and how we should build visual BCI with higher information rate. Using information theory, we estimate a maximum achievable ITR of approximately 63 bits per second (bps) with a uniformly-distributed White Noise (WN) stimulus. Based on this discovery, we propose a broadband WN BCI approach that expands the utilization of stimulus bandwidth, in contrast to the current state-of-the-art visual BCI methods based on steady-state visual evoked potentials (SSVEPs). Through experimental validation, our broadband BCI outperforms the SSVEP BCI by an impressive margin of 7 bps, setting a new record of 50 bps. This achievement demonstrates the possibility of decoding 40 classes of noninvasive neural responses within a short duration of only 0.1 seconds. The information-theoretical framework introduced in this study provides valuable insights applicable to all sensory-evoked BCIs, making a significant step towards the development of next-generation human-machine interaction systems.
    Keywords Computer Science - Human-Computer Interaction ; Computer Science - Information Theory ; Electrical Engineering and Systems Science - Signal Processing ; Quantitative Biology - Neurons and Cognition
    Publishing date 2023-08-25
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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