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Multi-band spatial feature extraction and classification for motor imaging EEG signals based on OSFBCSP-GAO-SVM model

EEG signal processing

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Abstract

Electroencephalogram (EEG) is a non-stationary random signal with strong background noise, which makes its feature extraction difficult and recognition rate low. This paper presents a feature extraction and classification model of motor imagery EEG signals based on wavelet threshold denoising. Firstly, this paper uses the improved wavelet threshold algorithm to obtain the denoised EEG signal, divides all EEG channel data into multiple partially overlapping frequency bands, and uses the common spatial pattern (CSP) method to construct multiple spatial filters to extract the characteristics of EEG signals. Secondly, EEG signal classification and recognition are realized by the support vector machine algorithm optimized by a genetic algorithm. Finally, the dataset of the third brain-computer interface (BCI) competition and the dataset of the fourth BCI competition is selected to verify the classification effect of the algorithm. The highest accuracy of this method for two BCI competition datasets is 92.86% and 87.16%, respectively, which is obviously superior to the traditional algorithm model. The accuracy of EEG feature classification is improved. It shows that an overlapping sub-band filter banks common spatial pattern-genetic algorithms optimization-support vector machines (OSFBCSP-GAO-SVM) model is an effective model for feature extraction and classification of motor imagination EEG signals.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grants 61563032 and 61963025. Project (2019KFJJ02) supported by Open Fund Project of Industrial Process Advanced Control of Gansu Province.

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, Gansu, China

    Yong Shang & Aimin An

  2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou, 730050, Gansu, China

    Xing Gao & Aimin An

  3. National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou, 730050, Gansu, China

    Aimin An

Authors
  1. Yong Shang
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  2. Xing Gao
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  3. Aimin An
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Correspondence to Aimin An.

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Shang, Y., Gao, X. & An, A. Multi-band spatial feature extraction and classification for motor imaging EEG signals based on OSFBCSP-GAO-SVM model. Med Biol Eng Comput 61, 1581–1602 (2023). https://doi.org/10.1007/s11517-023-02793-3

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  • DOI: https://doi.org/10.1007/s11517-023-02793-3

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