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Improving Motor Imagery Brain-Computer Interface Performance Through Data Screening

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Intelligent Robotics and Applications (ICIRA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14267))

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Abstract

Brain-computer interface (BCI) technology enables the direct transmission of human control intentions to external devices, allowing direct control of external devices through the human brain. However, the current implementation of BCIs is limited by the low accuracy of electroencephalogram (EEG) classification. In this study, we applied Gaussian distribution model as a preprocessing tool to screen and filter EEG training data samples, aiming to improve the classification accuracy of motor imagery tasks. Firstly, the Gaussian distribution model was established through small sample pre-training. Subsequently, a probability threshold was determined based on the two types of Gaussian model distributions corresponding to the imagery of the left and right hands. This threshold was used to screen and filter subsequent training samples. Our results demonstrated that this proposed method effectively enhanced the accuracy of motor imagery task classification, and significant improvements were observed in public datasets. This study emphasizes the importance of data screening in ensuring the quality and reliability of training data, thereby presenting promising opportunities for the practical implementation of BCI technology.

S. Zheng and L. Jiang—These authors contributed equally.

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Acknowledgment

This work was supported by the National Natural Science Foundation of China (12101570), Zhejiang Lab & Pujiang Lab (K2023KA1BB01), and Key Research Project of Zhejiang Lab (2022KI0AC01).

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Authors and Affiliations

  1. Research Institute of Artificial Intelligence, Zhejiang Lab, Hangzhou, 311100, Zhejiang, China

    Shiwei Zheng, Lizi Jiang, Xun Mai, Tao Tang, Linqing Feng, Feng Lin & Yina Wei

  2. Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027, China

    Tao Tang, Linqing Feng & Yina Wei

Authors
  1. Shiwei Zheng
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  2. Lizi Jiang
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  3. Xun Mai
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  4. Tao Tang
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  5. Linqing Feng
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  6. Feng Lin
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  7. Yina Wei
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Corresponding author

Correspondence to Yina Wei .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Huayong Yang

  2. Harbin Institute of Technology, Shenzhen, China

    Honghai Liu

  3. Zhejiang University, Hangzhou, China

    Jun Zou

  4. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  5. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  6. Zhejiang University, Hangzhou, China

    Geng Yang

  7. Zhejiang University, Hangzhou, China

    Xiaoping Ouyang

  8. Harbin Institute of Technology, Shenzhen, China

    Zhiyong Wang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zheng, S. et al. (2023). Improving Motor Imagery Brain-Computer Interface Performance Through Data Screening. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14267. Springer, Singapore. https://doi.org/10.1007/978-981-99-6483-3_20

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  • DOI: https://doi.org/10.1007/978-981-99-6483-3_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-6482-6

  • Online ISBN: 978-981-99-6483-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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