An MVMD-CCA Recognition Algorithm in SSVEP-Based BCI and Its Application in Robot Control

Kang Wang; Di Hua Zhai; Yuhan Xiong; Leyun Hu; Yuanqing Xia

doi:10.1109/TNNLS.2021.3135696

An MVMD-CCA Recognition Algorithm in SSVEP-Based BCI and Its Application in Robot Control

Kang Wang, Di Hua Zhai^*, Yuhan Xiong, Leyun Hu, Yuanqing Xia

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

28 Citations (Scopus)

Abstract

This article proposes a novel recognition algorithm for the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) system. By combining the advantages of multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA), an MVMD-CCA algorithm is investigated to improve the detection ability of SSVEP electroencephalogram (EEG) signals. In comparison with the classical filter bank canonical correlation analysis (FBCCA), the nonlinear and non-stationary EEG signals are decomposed into a fixed number of sub-bands by MVMD, which can enhance the effect of SSVEP-related sub-bands. The experimental results show that MVMD-CCA can effectively reduce the influence of noise and EEG artifacts and improve the performance of SSVEP-based BCI. The offline experiments show that the average accuracies of MVMD-CCA in the training dataset and testing dataset are improved by 3.08% and 1.67%, respectively. In the SSVEP-based online robotic manipulator grasping experiment, the recognition accuracies of the four subjects are 92.5%, 93.33%, 90.83%, and 91.67%, respectively.

Original language	English
Pages (from-to)	2159-2167
Number of pages	9
Journal	IEEE Transactions on Neural Networks and Learning Systems
Volume	33
Issue number	5
DOIs	https://doi.org/10.1109/TNNLS.2021.3135696
Publication status	Published - 1 May 2022

Keywords

Brain-computer interface (BCI)
canonical correlation analysis (CCA)
electroencephalogram (EEG)
multivariate variational mode decomposition (MVMD)
steady-state visual evoked potentials (SSVEP)

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10.1109/TNNLS.2021.3135696

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title = "An MVMD-CCA Recognition Algorithm in SSVEP-Based BCI and Its Application in Robot Control",

abstract = "This article proposes a novel recognition algorithm for the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) system. By combining the advantages of multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA), an MVMD-CCA algorithm is investigated to improve the detection ability of SSVEP electroencephalogram (EEG) signals. In comparison with the classical filter bank canonical correlation analysis (FBCCA), the nonlinear and non-stationary EEG signals are decomposed into a fixed number of sub-bands by MVMD, which can enhance the effect of SSVEP-related sub-bands. The experimental results show that MVMD-CCA can effectively reduce the influence of noise and EEG artifacts and improve the performance of SSVEP-based BCI. The offline experiments show that the average accuracies of MVMD-CCA in the training dataset and testing dataset are improved by 3.08% and 1.67%, respectively. In the SSVEP-based online robotic manipulator grasping experiment, the recognition accuracies of the four subjects are 92.5%, 93.33%, 90.83%, and 91.67%, respectively.",

keywords = "Brain-computer interface (BCI), canonical correlation analysis (CCA), electroencephalogram (EEG), multivariate variational mode decomposition (MVMD), steady-state visual evoked potentials (SSVEP)",

author = "Kang Wang and Zhai, {Di Hua} and Yuhan Xiong and Leyun Hu and Yuanqing Xia",

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AU - Zhai, Di Hua

AU - Xiong, Yuhan

AU - Hu, Leyun

AU - Xia, Yuanqing

PY - 2022/5/1

Y1 - 2022/5/1

N2 - This article proposes a novel recognition algorithm for the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) system. By combining the advantages of multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA), an MVMD-CCA algorithm is investigated to improve the detection ability of SSVEP electroencephalogram (EEG) signals. In comparison with the classical filter bank canonical correlation analysis (FBCCA), the nonlinear and non-stationary EEG signals are decomposed into a fixed number of sub-bands by MVMD, which can enhance the effect of SSVEP-related sub-bands. The experimental results show that MVMD-CCA can effectively reduce the influence of noise and EEG artifacts and improve the performance of SSVEP-based BCI. The offline experiments show that the average accuracies of MVMD-CCA in the training dataset and testing dataset are improved by 3.08% and 1.67%, respectively. In the SSVEP-based online robotic manipulator grasping experiment, the recognition accuracies of the four subjects are 92.5%, 93.33%, 90.83%, and 91.67%, respectively.

AB - This article proposes a novel recognition algorithm for the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) system. By combining the advantages of multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA), an MVMD-CCA algorithm is investigated to improve the detection ability of SSVEP electroencephalogram (EEG) signals. In comparison with the classical filter bank canonical correlation analysis (FBCCA), the nonlinear and non-stationary EEG signals are decomposed into a fixed number of sub-bands by MVMD, which can enhance the effect of SSVEP-related sub-bands. The experimental results show that MVMD-CCA can effectively reduce the influence of noise and EEG artifacts and improve the performance of SSVEP-based BCI. The offline experiments show that the average accuracies of MVMD-CCA in the training dataset and testing dataset are improved by 3.08% and 1.67%, respectively. In the SSVEP-based online robotic manipulator grasping experiment, the recognition accuracies of the four subjects are 92.5%, 93.33%, 90.83%, and 91.67%, respectively.

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KW - canonical correlation analysis (CCA)

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An MVMD-CCA Recognition Algorithm in SSVEP-Based BCI and Its Application in Robot Control

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