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A Calibration-free Approach to Implementing P300-based Brain–computer Interface

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Abstract

Introduction: As a direct bridge between the brain and the outer world, brain–computer interface (BCI) is expected to replace, restore, enhance, supplement, or improve the natural output of brain. The prospect of BCI serving humans is very broad. However, the extensive applications of BCI have not been fully achieved. One of reasons is that the cost of calibration reduces the convenience and usability of BCI. Methods: In this study, we proposed a calibration-free approach, which is based on the ideas of reinforcement learning and transfer learning, for P300-based BCI. This approach, composed of two algorithms: P300 linear upper confidence bound (PLUCB) and transferred PLUCB (TPLUCB), is able to learn during the usage by exploration and exploitation and allows P300-based BCI to start working without any calibration. Results: We tested the performances of PLUCB and TPLUCB using stepwise linear discriminant analysis (SWLDA), a commonly used method that needs calibration, as a baseline in simulated online experiments. The results showed the merits of PLUCB and TPLUCB. PLUCB can quickly increase the accuracies to the level of SWLDA. TPLUCB has surpassed SWLDA in the sample accuracy since it starts running. Both PLUCB and TPLUCB have the ability to keep improving the classification performance during the process. The overall sample accuracies (\(73.6\pm 4.8\%\), \(73.1\pm 4.9\%\)), overall symbol accuracies (\(80.4\pm 12.8\%\), \(79.6\pm 14.0\%\)), F-measures (\(0.45\pm 0.06\), \(0.44\pm 0.06\)) and information transfer ratios (ITR) (\(36.4\pm 9.1\), \(35.5\pm 9.8\)) of PLUCB and TPLUCB are significantly better than those of SWLDA (overall sample accuracy: \(58.8\pm 3.8\%\), overall symbol accuracy: \(69.0\pm 18.3\%\), F-measure: \(0.38\pm 0.04\), ITR: \(28.7\pm 10.7\)). Conclusions: The proposed approach, which does not need calibration but outperform SWLDA, is a very good option for the implementation of P300-based BCI.

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Acknowledgements

Zhilei Lv, Faqiang Peng and Ting Li participated in this work when they studied at Fuzhou University. We thank them for their contributions.

Funding

This study was supported by the Transformation Project of Scientific and Technological Achievements of Fuzhou, China (2020-GX-12), the Natural Science Foundation of Fujian Province,China (2019J01242) and  the National Natural Science Foundation of China (62076064).

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Authors and Affiliations

  1. College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China

    Zhihua Huang, Jiannan Guo & Yingjie Wu

  2. Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, 210096, China

    Wenming Zheng

  3. Department of Neurosurgery, Sanbo Brain Hospital of Capital Medical University, Beijing, 100093, China

    Zhixiong Lin

  4. School of Computing, Ulster University, Belfast, UK

    Huiru Zheng

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  1. Zhihua Huang
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  2. Jiannan Guo
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Correspondence to Zhihua Huang.

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All procedures performed in the study involving human participants were in accordance with the ethical standards of the Institutional Review Board at Fuzhou University and with the 1964 Helsinki declaration and its later amendments.

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Huang, Z., Guo, J., Zheng, W. et al. A Calibration-free Approach to Implementing P300-based Brain–computer Interface. Cogn Comput 14, 887–899 (2022). https://doi.org/10.1007/s12559-021-09971-1

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