Steady-state visual evoked potential classification algorithm based on MVMDMS-CCA
10.3969/j.issn.1005-202X.2025.07.014
- VernacularTitle:基于MVMDMS-CCA的稳态视觉诱发电位分类算法
- Author:
Zihao NIE
1
;
Raofen WANG
1
Author Information
1. 上海工程技术大学电子电气工程学院,上海 201620
- Publication Type:Journal Article
- Keywords:
multivariate variational mode decomposition;
steady-state visual evoked potential;
mode selection;
canonical correlation analysis
- From:
Chinese Journal of Medical Physics
2025;42(7):935-944
- CountryChina
- Language:Chinese
-
Abstract:
Considering the classification problems of electroencephalogram(EEG)signals and their nonlinear,non-stationary characteristics,multivariate variational mode decomposition(MVMD)is introduced to process steady-state visual evoked potential(SSVEP)signals.Herein a novel classification algorithm for SSVEP called MVMDMS-CCA which combines a new approach for mode selection with canonical correlation analysis(CCA)algorithm is presented.MVMDMS-CCA method uses the signal-to-noise ratio to determine the key parameter K in MVMD,and then performs MVMD decomposition.Mode selection is carried out by setting a threshold using the maximal information coefficient(MIC)method,and the modes not meeting the threshold are adaptively denoised using wavelet denoising.A new combination of modes is constructed and input into the CCA algorithm to achieve SSVEP signal classification.The proposed method is validated on a self-collected EEG dataset,and it achieves an average classification accuracy of 93.23%under a 3 s window,showing 5.78%higher than standard CCA and 1.51%higher than the improved filter bank CCA.MVMDMS-CCA can effectively extract SSVEP components from EEG signals while suppressing noises,providing a new perspective for the research of SSVEP decoding algorithms.
