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Optimizing motion imagery classification with limited channels using the common spatial pattern-based integrated algorithm

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Abstract

The extraction of effective classification features from electroencephalogram (EEG) signals in motor imagery is a popular research topic. The Common Spatial Pattern (CSP) algorithm is widely employed in this field. However, the performance of the traditional CSP method depends significantly on the choice of a specific frequency band and channel number of EEG data. Furthermore, inter-class variance among these frequency bands and the limited number of available EEG channels can adversely affect the CSP algorithm’s ability to extract meaningful features from the relevant signal frequency bands. We hypothesize that multiple Intrinsic Mode Functions (IMFS), into which the raw EEG signal is decomposed, can better capture the non-Gaussian characteristics of the signal, thus compensating for the limitations of the CSP algorithm when dealing with nonlinear and non-Gaussian distributed data with few channels. Therefore, this paper proposes a novel method that integrates Variational Mode Decomposition (VMD), Phase Space Reconstruction (PSR), and the CSP algorithm to address these issues. VMD is used to filter and enhance the quality of the collected data, PSR is employed to increase the effective data channels (data augmentation), and the subsequent CSP filtering can obtain signals with spatial features, which are decoded by Convolutional Neural Networks (CNN) for action decoding. This study utilizes self-collected EEG data to demonstrate that the new method can achieve a good classification accuracy of 82.30% on average, confirming the improved algorithm’s effectiveness and feasibility. Furthermore, this study conducted validation on the publicly available BCI Competition IV dataset 2b, demonstrating an average classification accuracy of 87.49%.

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Funding

This work was supported by the National Key Research and Development Program of China (No. 2021ZD0113204), National Natural Science Foundation of China (Nos. 62371178 and 62301197), and Zhejiang Provincial Key Research and Development Program of China (No.2024C03041), Zhejiang Provincial Natural Science Foundation of China (NO. LQ21H180005).

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Authors and Affiliations

  1. School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China

    Shishi Chen, Xugang Xi, Ting Wang, Lihua Li & Zhong Lü

  2. Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, 310018, China

    Shishi Chen, Xugang Xi & Ting Wang

  3. Hangzhou Mingzhou Naokang Rehabilitation Hospital, Hangzhou, 311215, China

    Hangcheng Li

  4. Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, 322100, China

    Maofeng Wang

Authors
  1. Shishi Chen
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  2. Xugang Xi
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  3. Ting Wang
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  4. Hangcheng Li
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  6. Lihua Li
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  7. Zhong Lü
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Contributions

Shishi Chen: Methodology, Writing—original draft, Data curation.

Xugang Xi: Funding acquisition, Writing—review & editing, Resources.

Ting Wang: Funding acquisition, Writing—review & editing.

Hangcheng Li and Maofeng Wang: Formal analysis, Visualization.

Lihua Li: Validation;

Corresponding author

Correspondence to Ting Wang.

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Ethics approval and consent to participate

All the experimental procedures were approved by the ethics committee of Dongyang People’s Hospital of Zhejiang Province.

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The authors declare no competing interests.

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Chen, S., Xi, X., Wang, T. et al. Optimizing motion imagery classification with limited channels using the common spatial pattern-based integrated algorithm. Med Biol Eng Comput (2024). https://doi.org/10.1007/s11517-024-03069-0

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